Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Stochastic disaggregation of daily rainfall based on K-Nearest neighbor resampling method

K번째 최근접 표본 재추출 방법에 의한 일 강우량의 추계학적 분해에 대한 연구

  • Park, HeeSeong (Hydro Science and Engineering Research Institute, Korea Institute of Civil Engineering and Building Technology) ;
  • Chung, GunHui (Department of Civil Engineering, Hoseo University)
  • 박희성 (한국건설기술연구원 수자원하천연구소) ;
  • 정건희 (호서대학교 건축토목환경공학부 토목공학전공)
  • Received : 2015.10.26
  • Accepted : 2016.02.17
  • Published : 2016.04.30
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Abstract

As the infrastructures and populations are the condensed in the mega city, urban flood management becomes very important due to the severe loss of lives and properties. For the more accurate calculation of runoff from the urban catchment, hourly or even minute rainfall data have been utilized. However, the time steps of the measured or forecasted data under climate change scenarios are longer than hourly, which causes the difficulty on the application. In this study, daily rainfall data was disaggregated into hourly using the stochastic method. Based on the historical hourly precipitation data, Gram Schmidt orthonormalization process and K-Nearest Neighbor Resampling (KNNR) method were applied to disaggregate daily precipitation into hourly. This method was originally developed to disaggregate yearly runoff data into monthly. Precipitation data has smaller probability density than runoff data, therefore, rainfall patterns considering the previous and next days were proposed as 7 different types. Disaggregated rainfall was resampled from the only same rainfall patterns to improve applicability. The proposed method was applied rainfall data observed at Seoul weather station where has 52 years hourly rainfall data and the disaggregated hourly data were compared to the measured data. The proposed method might be applied to disaggregate the climate change scenarios.

산업의 발전에 따라 기반시설 및 인구 등이 대도시에 밀집되어, 도시홍수방어는 인명피해 뿐만 아니라 재산피해 저감 차원에서도 매우 중요한 문제가 되었다. 요즘은 이러한 도시유역의 유출해석을 보다 정확하게 하기 위해 시강우나 분단위의 강우자료를 활용하고 있다. 하지만 기후변화 시나리오와 같은 미래 강우시나리오는 현재 일단위 수준으로 제공되므로 미래 강우에 대한 확률빈도 해석에 제한이 있다. 이에 본 연구에서는 추계학적 기법을 이용해 일강우 자료를 시강우 자료로 분해하고자 하였다. 일자료를 시자료로 분해하기 위해 과거 시강우 자료를 기반으로 Gram Schmidt 변환과 K개의 최근접 표본 중 하나를 재추출하는 비모수적인 기법(KNNR)을 적용하였다. 이 방법은 연유출량을 월유출량으로 분해하기 위해 개발된 것이다. 하지만 강우자료는 유출량 자료와 달리 확률밀도가 작아 일강우를 시강우로 분해하는 데 직접 적용하는 경우 결과가 실제와 유사한 통계 패턴을 갖는다고 보기 어려웠다. 이를 보완하기 위해 본 연구에서는 분해하고자 하는 일자의 전일과 후일을 포함한 3일 강우패턴을 7개로 구분하고 동일 패턴을 가진 자료들만 분해에 이용하도록 하여 강우자료에 대한 적용성을 높였다. 과거 52년간의 서울기상관측소 시강우 자료를 이용하여 강우자료의 분해에 대한 결과를 분석한 결과, 분해된 시강우 자료가 관측된 시강우자료와 통계적으로 매우 유사한 것을 확인하였다. 향후 기후변화자료의 시강우 분해 등에 활용하여 보다 정확한 도시유출에 대한 빈도해석 등에 적용할 수 있을 것으로 판단된다.

Keywords

References

  1. Kyoung, M.S., Sivakumar, B., Kim, H.S., and Kim, B.S., (2008). "Chaotic Disaggregation of Daily Rainfall from the Climate Change Scenarios" Proceedings of Korea Water Resources Association, pp. 178-183.
  2. Kim, B.S., Kim, B.K., Kyung, M.S., and Kim, H.S., (2008). "Impact Assessment of Climate Change on Extreme Rainfall and I-D-F Analysis." Journal of Korea Water Resources Association, Vol. 41, No. 4, pp. 379-394. https://doi.org/10.3741/JKWRA.2008.41.4.379
  3. Kim, T.J., Kwon, H.H., Lee, D.R., 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
  4. Kumar, D.N., Lall, U., and Peterson, M.R. (2000). "Multisite disaggregation of monthly to daily streamflow." Water Resources Research, Vol. 36, No. 7, pp. 1823-1833. https://doi.org/10.1029/2000WR900049
  5. Lall, U., and Sharma, A. (1996). "A nearest neighbor bootstrap for resampling hydrologic time series." Water Resources Research, Vol. 32, No. 3, pp. 679-693. https://doi.org/10.1029/95WR02966
  6. Lee, T., Salas, J.D., and Prairie, J. (2010). "An enhanced nonparametric streamflow disaggregation model with genetic algorithm." Water Resources Research, Vol. 46, W08545.
  7. Lee, T., and Jeong, C., (2014). "Nonparametric statistical temporal downscaling of daily precipitation to hourly precipitation and implications for climate change scenarios." Journal of Hydrology, Vol. 510, pp. 182-196. https://doi.org/10.1016/j.jhydrol.2013.12.027
  8. Lee, T., Park, T., Lee, H., and Jeong, C. (2014). "Temporal Downscaling of Precipitation from Daily to Hourly Based on Nonparametric Approach: Assessment of the Climate Change Impacts on the Hourly Precipitation for the Gyeongnam Region." Journal of Korean Society of Hazard Mitigation, Vol. 14, No. 4, pp. 301-308. https://doi.org/10.9798/KOSHAM.2014.14.4.301
  9. Liou, E.Y. (1970). OPSET-Program for computerized selection of watershed parameter values of the Stanford watershed model, Research Report No. 34, University of Kentucky Water Resources Institute, Lexington, KY.
  10. Matalas, N.C. (1967). "Mathematical assessment of synthetic hydrology." Water Resources Research, Vol. 3, No. 4, pp. 937-946. https://doi.org/10.1029/WR003i004p00937
  11. Mandelbrot, B.B., and Wallis, J.R. (1968). "Noah, Josep, and operational hydrology." Water Resources Research, Vol. 4, No. 5, pp. 909-918. https://doi.org/10.1029/WR004i005p00909
  12. Mandelbrot, B.B., and Wallis, J.R. (1969a). "Computer experiments with fractional Gaussian noises, 1. averages and variances." Water Resources Research, Vol. 5, No. 1, pp. 228-241. https://doi.org/10.1029/WR005i001p00228
  13. Mandelbrot, B.B., and Wallis, J.R. (1969b). "Computer experiments with fractional Gaussian noises, 2. Rescaled ranges and spectra." Water Resources Research, Vol. 5, No. 1, pp. 242-259. https://doi.org/10.1029/WR005i001p00242
  14. Mandelbrot, B.B., and Wallis, J.R. (1969c). "Some longrun properties of geophysical records." Water Resources Research, Vol. 5, No. 2, pp. 321-340. https://doi.org/10.1029/WR005i002p00321
  15. Ormsbee (1989). "Rainfall disaggregation model for continuous hydrologic modeling." Journal of Hydraulic Engineering, Vol. 115, pp. 507-525. https://doi.org/10.1061/(ASCE)0733-9429(1989)115:4(507)
  16. Prairie, J.R., Rajagopalan, B. Lall, U., and Fulp, T. (2007). "A stochastic nonparametric technique for space-time disaggregation of streamflows." Water Resources Research, Vol. 43, W03432, doi:10.1029/2005WR004721.
  17. Rodriguez-Iturbe, I., Mejia, J.M., and Dawdy, D.R. (1972). "Streamflow simulation, 1, A new look at markovian models, fractional Gaussian noise and crossing theory." Water Resources Research, Vol. 8, No. 4, pp. 921-930. https://doi.org/10.1029/WR008i004p00921
  18. Sharma, A., and O'Neill, R. (2002). "A nonparametric approach for representing interannual dependence in monthly streamflow." Water Resources Research, Vol. 138, No. 7, pp. 5-1.
  19. Tarboton, D.G., Sharma, A., and Lall, U. (1998). "Disaggregation procedures for stochastic hydrology based on nonparametric density estimation." Water Resources Research, Vol. 34, No. 1, pp. 107-119. https://doi.org/10.1029/97WR02429
  20. Valencia, D.R., and Schaake, J.C. (1973). "Disaggregation process in stochastic hydrology." Water Resources Research, Vol. 9, No. 3, pp. 580-585. https://doi.org/10.1029/WR009i003p00580

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