DOI QR코드

DOI QR Code

Seasonal Rainfall Outlook of Nakdong River Basin Using Nonstationary Frequency Analysis Model and Climate Information

기상인자와 비정상성 빈도해석 모형을 이용한 낙동강유역의 계절강수량 전망

  • Received : 2011.02.21
  • Accepted : 2011.04.19
  • Published : 2011.05.31

Abstract

This study developed a climate informed Bayesian nonstationary frequency model which allows us to forecast seasonal summer rainfall at Nakdong River. We constructed a 37-year summer rainfall data set from 10 weather stations within Nakdong river basin, and two climate indices from sea surface temperature (SST) and outgoing longwave radiation (OLR) were derived through correlation analysis. The selected SST and OLR have been widely acknowledged as a climate driver for summer rainfall. The developed model was applied first to the 2010-year summer rainfall (888.1 mm) in order to assure ourself. We demonstrated model performance by comparing posterior distributions. It was confirmed that the proposed model is able to produce a reasonable forecast. The forecasted value is about 858.2 mm, and the difference between forecast and observation is about 30 mm. As the second case study, 2011-year summer rainfall forecast was made using an observed winter SSTs and an assumed 50% value of OLRs. The forecasted value is 967.7 mm and associated exceedance probability over average summer rainfall 680 mm is 92.9%. In addition, 50-year return period for summer rainfall was projected through the nonstationary frequency model. An exceedance probability over 1,400 mm corresponding to the 50-year return level is about 73.7%.

본 연구에서는 Bayesian 통계기법을 이용한 비정상성 빈도해석모형을 토대로 외부 기상인자에 의한 변동성을 고려할 수 있는 계절강수량 예측모형을 구축하였으며, 낙동강유역내의 10개 관측소에서 관측된 37년간의 강수량 자료를 이용하여 연도별 여름강수량을 추출하고 이들 관측소의 여름강수량에 물리적인 영향을 미치는 기상인자로서 SST(sea surface temperature)와 OLR(outgoing longwave radiation)을 공간상관성을 검토하여 선정하였다. 모형의 적합성을 검토하기 위해 2010년 여름강수량 사후 확률분포의 중앙값과 관측치를 비교하였으며, 그 결과 각각 858.2mm와 888.1mm로, 이는 구축된 모형이 적절하게 여름강수량을 모의하고 있음을 보여준다. 2010년 겨울 SST 관측 값과, 예년 평균값으로 가정한 2011년 6월 OLR을 이용하여 2011년 여름강수량을 예측하였다. 예측된 2011년 여름강수량은 967.7mm로, 확률적으로 예년 여름강수량의 평균인 680mm를 상회할 확률이 92.9% 이상인 것으로 나타났으며, 또한 50년 빈도에 해당하는 여름강수량을 추정한 결과, 50년 빈도 여름강수량 1400mm를 상회할 확률도 약 73.7%인 것으로 분석되었다.

Keywords

References

  1. 권현한, 문영일(2005). "Nino3.4지역 SST 및 여름강수량의 독립성분분석." 한국수자원학회논문집, 한국수자 원학회, 제38권, 제12호, pp. 985-994. https://doi.org/10.3741/JKWRA.2005.38.12.985
  2. 권현한, 문영일(2007). "기상정보 및 태풍특성을 고려한 계절 강수량의 확률론적 모형 구축." 대한토목학회논문집, 대한토목학회, 제27권, 제1B호, pp. 45-52.
  3. 김맹기, 김화수, 곽종흠, 소선섭, 서명석, 박정규(2002). "광역규모 예측인자를 이용한 한반도 계절 강수량의 장기 예측." Journal of Korean Earth Science Society, 한국지구과학회, Vol. 23, No. 7, pp. 587-596.
  4. 김호준, 백희정, 권원태(1999). "지역별 중장기 강수량 예 측을 위한 신경망 기법." 한국지리정보학회지, 한국지리정보학회, 제2권, 제2호, pp. 69-78.
  5. 안중배, 류정희, 조익현, 박주영, 류상범(1997). "한반도 기온 및 강수량과 주변 해역 해면 온도와의 상관관계에 관한 연구." 한국기상학회지, 한국기상학회, 제33권, 제2호, pp. 327-336.
  6. 이정주(2010). 강수계열의 비정상성 평가 및 비정상성 빈도해석 기법 개발. 박사학위논문, 전북대학교.
  7. 이정주, 권현한, 김태웅(2010). "극치수문자료의 경향성 분석 개념 및 비정상성 빈도해석." 대한토목학회논문집, 대한토목학회, 제30권, 제4B호, pp. 389-397.
  8. Bansod, S.D. (2004). "Outgoing long-wave radiation over the Tropical Pacific and Atlantic Ocean and Indian summer monsoon rainfall." Theoretical and Applied Climatology, Vol. 77, No. 3-4, pp. 185-193. https://doi.org/10.1007/s00704-003-0031-6
  9. Chang, C.P., Zhang, Y.S., and Li, T. (2000). "Interannual and interdecadal variations of the East Asian summer monsoon and the tropical Pacific SSTs. Part II: Meridional structure of the monsoon." Journal of Climate, Vol. 13, No. 24, pp. 4326-4340. https://doi.org/10.1175/1520-0442(2000)013<4326:IAIVOT>2.0.CO;2
  10. Clark, C.O., Cole, J.E., and Webster, P.J. (2000). "Indian Ocean SST and Indian summer rainfall: Predictive relationships and their decadal variability." Journal of Climate, Vol. 13, pp. 2503-2518. https://doi.org/10.1175/1520-0442(2000)013<2503:IOSAIS>2.0.CO;2
  11. Coles, S., Pericchi, L.R., and Sisson, S. (2003). "A fully probabilistic approach to extreme rainfall modeling." Journal of Hydrology, Vol. 273, pp. 35-50. https://doi.org/10.1016/S0022-1694(02)00353-0
  12. Kang, I.-S., Lee, D.-I., and Min, K.-D. (1991). "Seasonal Evolution of Summer Precipitation and Moisture Transport in Asian Monsoon Region Estimated from the ECMWF Data." Journal of the Korean Meteorological Society, Vol. 27, No. 3, pp. 241-255
  13. Krishnamurthy, V., and Kirtman, B.P. (2009). "Relation between Indian Monsoon Variability and SST." Journal of Climate, Vol. 22, No. 17, pp. 4437-4458. https://doi.org/10.1175/2009JCLI2520.1
  14. Latif, M., Sterl, A., Assenbaum, M., Junge, M.M., and Maierreimer, E. (1994). "Climate variability in a coupled GCM. Part II: The Indian Ocean and monsoon." Journal of Climate, Vol. 7, No. 10, pp. 1449-1462. https://doi.org/10.1175/1520-0442(1994)007<1449:CVIACG>2.0.CO;2
  15. Lu, R.Y. (2004). "Associations among the components of the east Asian summer monsoon system in the meridional direction." Journal of Meteorological Society of Japan, Vol. 82, No. 1, 155-165. https://doi.org/10.2151/jmsj.82.155
  16. Wang, B., Wu, R.G., and Fu, X.H. (2000). "Pacific-East Asian teleconnection: How does ENSO affect East Asian climate?" Journal of Climate, Vol. 13, No. 9, pp. 1517-1536. https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2
  17. Wu, R.G., Hu, Z.Z., and Kirtman, B.P. (2003). "Evolution of ENSO-related rainfall anomalies in East Asia." Journal of Climate, Vol. 16, No. 22, pp. 3742- 3758. https://doi.org/10.1175/1520-0442(2003)016<3742:EOERAI>2.0.CO;2

Cited by

  1. Comparison Study on the Various Forms of Scale Parameter for the Nonstationary Gumbel Model vol.48, pp.5, 2015, https://doi.org/10.3741/JKWRA.2015.48.5.331
  2. Atmospheric Circulation of Pacific-Japan (PJ) and Typhoon-induced Extremes in the Nakdong River Basin vol.45, pp.12, 2012, https://doi.org/10.3741/JKWRA.2012.45.12.1309
  3. Improvement of Hydrologic Dam Risk Analysis Model Considering Uncertainty of Hydrologic Analysis Process vol.47, pp.10, 2014, https://doi.org/10.3741/JKWRA.2014.47.10.853
  4. Nonstationary Frequency Analysis Using a Hierarchical Bayesian Model vol.15, pp.5, 2015, https://doi.org/10.9798/KOSHAM.2015.15.5.19
  5. A Development of Regional Frequency Model Based on Hierarchical Bayesian Model vol.46, pp.1, 2013, https://doi.org/10.3741/JKWRA.2013.46.1.13
  6. A Study on the Changes of Return Period Considering Nonstationarity of Rainfall Data vol.47, pp.5, 2014, https://doi.org/10.3741/JKWRA.2014.47.5.447