한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제51권3호
  • /
  • Pages.221-233
  • /
  • 2018
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
권호 표지
DOI QR코드

DOI QR Code

레이더 강우 앙상블과 유출 블랜딩 기법을 이용한 최적 유출 수문곡선 산정

Estimation of optimal runoff hydrograph using radar rainfall ensemble and blending technique of rainfall-runoff models

  • Lee, Myungjin (Department of Civil Engineering, Inha University) ;
  • Kang, Narae (Korea Institute of Civil Engineering and Building Technology (KICT)) ;
  • Kim, Jongsung (Department of Civil Engineering, Inha University) ;
  • Kim, Hung Soo (Department of Civil Engineering, Inha University)
  • 투고 : 2017.10.30
  • 심사 : 2017.12.13
  • 발행 : 2018.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 기후변화로 인한 국지성 호우 및 태풍 피해가 자주 발생하고 있다. 이와 같은 피해를 저감하기 위해서는 정확한 강우의 예측과 홍수량 산정이 필요하다. 그러나 지점 및 레이더 강우 시 공간적 오차를 포함하고 있고, 유출 모형에 의한 유출수문곡선 역시 보정을 실시하더라도 관측유량과 오차를 가지고 있어 불확실성이 존재한다. 따라서 본 연구에서는 확률론적 강우 앙상블을 생성하여 강우의 불확실성을 확인하였다. 또한 유출 결과를 통해 수문 모형의 불확실성을 확인하였고, 블랜딩 기법을 이용하여 하나의 통합된 유출 수문곡선을 제시하였다. 생성된 강우앙상블은 강우강도 및 지형적인 영향으로 레이더가 과소 관측이 될 때, 강우 앙상블의 불확실성이 큰 것을 확인하였고, 블랜딩 기법을 적용하여 산정된 최적 유출 수문곡선은 유출모형의 불확실성을 크게 줄이는 것으로 나타났다. 본 연구 결과를 활용한다면, 정확한 홍수량 산정 및 예측을 통해 집중호우로 인한 피해를 줄일 수 있을 것으로 판단된다.

Recently, the flood damage by the localized heavy rainfall and typhoon have been frequently occurred due to the climate change. Accurate rainfall forecasting and flood runoff estimates are needed to reduce such damages. However, the uncertainties are involved in guage rainfall, radar rainfall, and the estimated runoff hydrograph from rainfall-runoff models. Therefore, the purpose of this study is to identify the uncertainty of rainfall by generating a probabilistic radar rainfall ensemble and confirm the uncertainties of hydrological models through the analysis of the simulated runoffs from the models. The blending technique is used to estimate a single integrated or an optimal runoff hydrograph by the simulated runoffs from multi rainfall-runoff models. The radar ensemble is underestimated due to the influence of rainfall intensity and topography and the uncertainty of the rainfall ensemble is large. From the study, it will be helpful to estimate and predict the accurate runoff to prepare for the disaster caused by heavy rainfall.

키워드

참고문헌

  1. Ajami, N. K., Duan, Q., Gao, X., and Sorooshian, S. (2006). "Multimodel combination techniques for hydrological forecasting: application to distributed model intercomparison project results." Journal of Hydrometeorol, Vol. 7, No. 4, pp. 755-768. https://doi.org/10.1175/JHM519.1
  2. Bae, Y. H., Kim, B. S., and Kim, H. S. (2010). "The study on flood runoff simulation using runoff model with gauge-adjusted radar data." Journal of Wetlands Research, Vol. 12, No. 1, pp. 51-61.
  3. Bates, J. M., and Granger, C. W. (1969). "The combination of forecasts." Journal of the Operational Research Society, Vol. 20, No. 4, pp. 451-468. https://doi.org/10.1057/jors.1969.103
  4. Chiang, Y. M., Chang, F. J., Dao Jou, B. J., and Lin, P. F. (2006). "Dynamic ANN for precipitiation estimation and forecasting from radar observation." Journal of Hydrology, Vol. 334, No. 1-2, pp. 250-261. https://doi.org/10.1016/j.jhydrol.2006.10.021
  5. Dai, Q., Han, D., Rico-Ramirez, M. A., and Islam, T. (2014). "Modelling radar-rainfall estimation uncertainties using elliptical and Archimedean copulas with different marginal distributions." Hydrological Sciences Journal, Vol. 59, No. 11, pp. 1992-2008. https://doi.org/10.1080/02626667.2013.865841
  6. Georgakakos, K. P., Seo, D. J., Gupta, H., Schaake, J., and Butts, M. B. (2004). "Towards the characterization of streamflow simulation uncertainty through multimodel ensembles." Journal of Hydrology, Vol. 298, No. 1, pp. 222-241. https://doi.org/10.1016/j.jhydrol.2004.03.037
  7. Germann, U., Berenguer, M., Sempere-Torres, D., and Salvade, G. (2006). "Ensemble radar precipitation - a new topic on the radar horizon." Proceeding of the 4th European Conference on Radar in Meteorology and Hydrology ERAD, Barcelona, Spain, September 2006, pp. 18-22.
  8. Kang, N. R., Joo, H. J., Lee, M. J., and Kim, H. S. (2017). "Generation of radar rainfall ensemble using probabilistic approach." Journal of Korea Water Resources Association, Vol. 50, No. 3, pp. 155-167. https://doi.org/10.3741/JKWRA.2017.50.3.155
  9. Kang, N. R., Noh, H. S., Lee, J. S., Lim, S. H., and Kim, H. S. (2013). "Runoff simulation of an urban drainage system using radar rainfall data." Journal of Wetlands Research, Vol. 15, No. 3, pp. 413-422. https://doi.org/10.17663/JWR.2013.15.3.413
  10. Kim, T. J., Lee, D. R., Jang, S. M., and Kwon, H. H. (2017). "Generation of radar rainfall data for hydrological and meteorological application (II): radar rainfall ensemble." Journal of Korea Water Resources Association, Vol. 50, No. 1, pp. 17-28. https://doi.org/10.3741/JKWRA.2017.50.1.17
  11. Krishnamurti, T. N., Kishtawal, C. M., Zhang, Z., LaRow, T., Bachiochi, D., and Williford, E. (2000). "Multimodel ensemble forecasts for weather and seasonal climate." Journal of Climate, Vol. 13, No. 23, pp. 4196-4216. https://doi.org/10.1175/1520-0442(2000)013<4196:MEFFWA>2.0.CO;2
  12. Li, W., and Sankarasubramanian, A. (2012). "Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination." Water Resources Research, Vol. 48, No. 12, pp. 1-17. https://doi.org/10.1016/j.advwatres.2012.07.002
  13. McLeod, A. I., Noakes, D. J., Hipel, K. W., and Thompstone, R. M. (1987). "Combining hydrologic forecast." Journal of Water Resources Planning and Management, Vol. 113, No. 1, pp. 29-41. https://doi.org/10.1061/(ASCE)0733-9496(1987)113:1(29)