Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of grid-type precipitation quantile using satellite based re-analysis precipitation data in Korean peninsula

위성 기반 재분석 강수 자료를 이용한 한반도 격자형 확률강수량 산정

  • Lee, Jinwook (Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University) ;
  • Jun, Changhyun (Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University) ;
  • Kim, Hyeon-joon (Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University) ;
  • Byun, Jongyun (Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University) ;
  • Baik, Jongjin (Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University)
  • 이진욱 (중앙대학교 건설환경플랜트공학과) ;
  • 전창현 (중앙대학교 건설환경플랜트공학과) ;
  • 김현준 (중앙대학교 건설환경플랜트공학과) ;
  • 변종윤 (중앙대학교 건설환경플랜트공학과) ;
  • 백종진 (중앙대학교 건설환경플랜트공학과)
  • Received : 2022.04.01
  • Accepted : 2022.05.17
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.

본 연구에서는 위성 기반 재분석 강수 자료인 PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record)을 이용하여 한반도에 대한 격자형 확률강수량을 산정하였다. 고려된 기간은 1983년부터 2020년까지 총 38개년이다. 사용된 자료의 공간해상도는 0.04°이며, 시간해상도는 3시간이다. 확률분포로는 빈도해석을 위해 일반적으로 사용되고 있는 Gumbel 분포를 사용하였으며, 매개변수 추정을 위해 확률가중모멘트법을 적용하였다. 지속기간은 3시간부터 144시간 까지, 재현기간은 2년부터 500년까지가 고려되었다. 이러한 방식으로 산정된 결과를 지상우량계인 ASOS (Automated Synoptic Observing System) 기상관측소의 강수 자료를 활용하여 산정된 확률강수량과 비교·검토하였다. 그 결과, PERSIANN-CCS-CDR 자료로부터 산정된 Gumbel 분포의 매개변수들은 지속기간이 증가함에 따라 ASOS의 결과들과 유사한 양상을 보였으며 이를 토대로 얻어진 확률강수량은 지속기간이 짧은 경우 다소 큰 차이를 보였으나, 지속기간이 18 h 이상인 경우 그 차이는 약 20% 이내로 감소함을 확인하였다. 추가적으로, 남북한 차이를 살펴보았으며 Gumbel 분포 매개변수들 중 위치 매개변수의 차이가 두드러지게 나타남을 확인하였다. 지속기간의 증가에 따른 북한의 확률강수량이 상대적으로 작게 나타났으며, 지속기간 3 h 기준 남한의 84%, 지속기간 144 h 기준 70~75% 수준인 것으로 확인되었다.

Keywords

Acknowledgement

이 성과는 과학기술정보통신부의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2021R1C1C2006215).

References

  1. Alila, Y. (1999). "A hierarchical approach for the regionalization of precipitation annual maxima in Canada." Journal of Geophysical Research, Vol. 104, No. D24, pp. 31645-31655. https://doi.org/10.1029/1999JD900764
  2. Courty, L.G., Wilby, R.L., Hillier, J.K., and Slater, L.J. (2019). "Intensity-duration-frequency curves at the global scale." Environmental Research Letters, Vol. 14, No. 8, 084045. https://doi.org/10.1088/1748-9326/ab370a
  3. Cunnane, C. (1989). Statistical distributions for flood frequency analysis. WMO Operational Hydrology Report No. 33, WMO Secretariat, Geneva, Switzerland.
  4. De Michele, C., and Rosso, R. (2001). "Uncertainty assessment of regionalized flood frequency estimates." Journal of Hydrologic Engineering, Vol. 6, No. 6, pp. 453-459. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:6(453)
  5. Fowler, H.J., and Kilsby, C.G. (2003). "A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000." International Journal of Climatology: A Journal of the Royal Meteorological Society, Vol. 23, No. 11, pp. 1313-1334. https://doi.org/10.1002/joc.943
  6. Gado, T.A., Hsu, K., and Sorooshian, S. (2017). "Rainfall frequency analysis for ungauged sites using satellite precipitation products." Journal of Hydrology, Vol. 554, pp. 646-655. https://doi.org/10.1016/j.jhydrol.2017.09.043
  7. Greenwood, J.A., Landwehr, J.M., Matalas, N.C., and Wallis, J.R. (1979). "Probability weighted moments: Definition and relation to parameters of several distributions expressible in inverse form." Water Resources Research, Vol. 15, No. 5, pp. 1049-1054. https://doi.org/10.1029/WR015i005p01049
  8. Heo, J.H., and Kim, H. (2019). "Regional frequency analysis for stationary and nonstationary hydrological data." Journal of Korea Water Resources Association, Vol. 52, No. 10, pp. 657-669. https://doi.org/10.3741/JKWRA.2019.52.10.657
  9. Heo, J.H., Lee, Y.S., Shin, H., and Kim, K.D. (2007). "Application of regional rainfall frequency alaysis in South Korea (I) Rainfall Quantile Estimation." Journal of the Korean Society of Civil Engineers B, Vol. 27, No. 2B, pp. 101-111.
  10. Hosking, J.R.M., and Wallis, J.R. (1986). "Paleoflood hydrology and flood frequency analysis." Water Resources Research, Vol. 22, No. 4, pp, 543-550. https://doi.org/10.1029/WR022i004p00543
  11. Hosking, J.R.M., and Wallis, J.R. (1997). Regional frequency analysis: An approach based on L-moments. Cambridge University Press, New York, U.S.
  12. Jeong, J.H. and Yoon, Y.N. (2007). Planning of water resource system, Goomi Press.
  13. Lee, D.J., and Heo, J.H. (2001). "Frequency analysis of daily rainfall in Han River Basin based on regional L-moments algorithm." Journal of Korea Water Resources Association, Vol. 34, No. 2, pp. 119-130.
  14. Lee, J.S. (2006). Hydrology, Goomi Press.
  15. Lee, Y.S. (2004). Regional frequency analysis of rainfall based on regional shape estimation and index flood methods. Master's Thesis. Department of Civil Engineering, Yonsei University.
  16. Lettenmaier, D.P., Wallis, J.R., and Wood, E.F. (1987). "Effect of regional heterogeneity on flood Frequency estimaion." Water Resources Research, Vol. 23, No. 2, pp. 313-323. https://doi.org/10.1029/WR023i002p00313
  17. Liang, Y., Liu, S., Guo, Y., and Hua, H. (2017). "L-Moment-Based regional frequency analysis of annual extreme precipitation and its uncertainty analysis." Water Resources Management, Vol. 31, No. 12, pp. 3899-3919. https://doi.org/10.1007/s11269-017-1715-5
  18. Lumbroso, D.M., Boyce, S., Bast, H., and Walmsley, N. (2011). "The challenges of developing rainfall intensity-duration-frequency curves and national flood hazard maps for the Caribbean." Journal of Flood Risk Management, Vol. 4, No. 1, pp. 42-52. https://doi.org/10.1111/j.1753-318X.2010.01088.x
  19. Ministry of Environment (ME) (2019). Standard guidelines for flood estimation.
  20. Ministry of Land, Transport and Maritime Affairs (MLTM) (2011). Improvement and supplement of probability rainfall.
  21. Ministry of Land, Transport and Maritime Affairs (MLTM) (2012). Design flood estimation guidelines.
  22. Nam, W., Shin, H., Jung, Y., Joo, K., and Heo, J.H. (2015). "Delineation of the climatic rainfall regions of South Korea based on a multivariate analysis and regional rainfall fequency analyses." International Journal of Climatology, Vol. 35, No. 5, pp. 777-793. https://doi.org/10.1002/joc.4182
  23. Nandakumar, N. (1995). Estimation of extreme rainfalls for Victoia : Application of the forge method. Cooperative Research Centre for Catchment Hydrology, Monash University, Clayton, Victoria, Australia.
  24. Overeem, A., Buishand, A., and Holleman, I. (2008). "Rainfall depthduration-frequency curves and their uncertainties." Journal of Hydrology, Vol. 348, No. 1-2, pp. 124-134. https://doi.org/10.1016/j.jhydrol.2007.09.044
  25. Papalexiou, S.M., and Koutsoyiannis, D. (2013). "Battle of extreme value distributions: A global survey on extreme daily rainfall." Water Resources Research, Vol. 49, No. 1, pp. 187-201. https://doi.org/10.1029/2012WR012557
  26. Sadeghi, M., Nguyen, P., Naeini, M.R., Hsu, K., Braithwaite, D., and Sorooshian, S. (2021). "PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies." Scientific Data, Vol. 8, No. 1, pp. 1-11. https://doi.org/10.1038/s41597-020-00786-7
  27. Schaefer, M.G. (1990). "Regional analyses of precipitation annual maxima in washington state." Water Resources Research, Vol. 26, No. 1, pp. 119-131. https://doi.org/10.1029/WR026i001p00119
  28. Wang, Z., Zeng, Z., Lai, C., Lin, W., Wu, X., and Chen, X. (2017). "A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c-means and L-moments approaches." International Journal of Climatology, Vol. 37, pp. 429-444. https://doi.org/10.1002/joc.5013
  29. Yin, Y., Chen, H., Xu, C.Y., Xu, W., Chen, C., and Sun, S. (2016). "Spatio-temporal characteristics of the extreme precipitation by L-moment-based index-flood method in the Yangtze River Delta region, China." Theoretical and Applied Climatology, Vol. 124, No. 3, pp. 1005-1022. https://doi.org/10.1007/s00704-015-1478-y
  30. Yoon, Y.N. (2007). Hydrology, Cheongmungag.