한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제44권3호
  • /
  • Pages.199-208
  • /
  • 2011
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
권호 표지
DOI QR코드

DOI QR Code

자료동화 기법을 연계한 실시간 하천유량 예측모형 개발

Development of Real-Time River Flow Forecasting Model with Data Assimilation Technique

  • 이병주 (국립기상연구소 응용기상연구과 수문자원연구팀) ;
  • 배덕효 (세종대학교 물자원연구소.토목환경공학과)
  • Lee, Byong-Ju (Hydrometeorological Resources Research Team, Applied Meteorology Research Division, National Institute of Meteorological Research) ;
  • Bae, Deg-Hyo (Dept. of Civil and Environmental Engineering, Sejong University)
  • 투고 : 2011.01.17
  • 심사 : 2011.03.02
  • 발행 : 2011.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 연속형 강우-유출모형과 앙상블 칼만 필터 기법을 연계하여 실시간 하천유량 예측모형을 개발하고 자료동화로 인한 정확도 개선 정도를 평가하고자 한다. 대상유역은 안동댐 상류유역을 선정하고 2006.7.1~8.18과 2007.8.1~9.30의 홍수기간에 대해 평가를 수행하였다. 자료동화를 위한 모형 상태변수는 유역의 토양수분과 저류량 및 하도 저류량을 선정하였으며 하류 댐 지점의 관측유량을 이용하여 상태변수를 갱신하도록 모형을 설계하였다. 상태변수의 칼만게인 거동을 분석한 결과 모의유량은 관측유량으로 74% 이동한 것으로 나타났다. 예측강우를 관측강우와 동일하다고 가정하고 예측선행시간 1시간에 대해 자료동화 전 후의 모의유량을 분석한 결과 2006년과 2007년에 각각 49.6%와 33.1%의 정확도가 향상됨을 확인하였다. 이상의 결과로부터 실시간 하천유량 예측시스템에 자료동화기법을 연계할 경우 강우-유출모형만을 이용한 결과보다 정확한 홍수량 예측이 가능할 것으로 판단된다.

The objective of this study is to develop real-time river flow forecast model by linking continuous rainfall-runoff model with ensemble Kalman filter technique. Andong dam basin is selected as study area and the model performance is evaluated for two periods, 2006. 7.1~8.18 and 2007. 8.1~9.30. The model state variables for data assimilation are defined as soil water content, basin storage and channel storage. This model is designed so as to be updated the state variables using measured inflow data at Andong dam. The analysing result from the behavior of the state variables, predicted state variable as simulated discharge is updated 74% toward measured one. Under the condition of assuming that the forecasted rainfall is equal to the measured one, the model accuracy with and without data assimilation is analyzed. The model performance of the former is better than that of the latter as much as 49.6% and 33.1% for 1 h-lead time during the evaluation period, 2006 and 2007. The real-time river flow forecast model using rainfall-runoff model linking with data assimilation process can show better forecasting result than the existing methods using rainfall-runoff model only in view of the results so far achieved.

키워드

참고문헌

  1. 김상호 (2003). “Kalman Filtering 기법을 이용한 수리학적 홍수예측.” 대한토목학회논문집, 대한토목학회, 제23권, 제6B호, pp. 541-549.
  2. 배덕효 (1997). “저류함수법을 이용한 추계학적 실시간 홍수예측모형 개발.” 한국수자원학회논문집, 한국수자원학회, 제30권, 제5호, pp. 449-457.
  3. 배덕효, 이병주 (2011). “대유역 홍수예측을 위한 연속형 강우-유출모형 개발.” 한국수자원학회논문집, 한국수자원학회, 제44권, 제1호, pp. 51-64.
  4. 배덕효, 이병주, Shamir E. (2009). “앙상블 칼만필터를 연계한 추계학적 연속형 저류함수모형 개발 ( I ): -모형개발-.” 한국수자원학회논문집, 한국수자원학회, 제42권, 제11호, pp. 953-961.
  5. 배덕효, 정일문 (2000). “저류함수법에 의한 추계동역학적 하도홍수추적모형의 개발.” 한국수자원학회논문집, 한국수자원학회, 제33권, 제3호, pp. 341-350.
  6. 안상진, 이재경, 한양수, 전계원 (2002). “유출예측모형을 이용한 홍수유출해석.” 대한토목학회논문집, 대한토목학회, 제22권, 제3-B호, pp. 311-319.
  7. 이병주, 배덕효, Shamir E. (2009). “앙상블 칼만필터를 연계한 추계학적 연속형 저류함수모형 개발 (II): -적용 및 검증-.” 한국수자원학회논문집, 한국수자원학회, 제42권, 제11호, pp. 963-972.
  8. 한건연, 손인호, 이재영 (2000). “실시간 범람위험도 예측을 위한 수리학적 모형의 개발.” 한국수자원학회논문집, 한국수자원학회, 제33권, 제3호, pp. 331-340.
  9. Arnold, J.G., Allen, P.M., and Bernhardt, G. (1993). “A comprehensive surface-groundwater flow model.” Journal of Hydrology, Vol. 142, pp. 47-69. https://doi.org/10.1016/0022-1694(93)90004-S
  10. Arnold, J.G., Srinivasan, R., Muttiah, R.S., and Willams, J.R. (1998). “Large area hydrologic modeling and assessment part I: model development.” Journal of the American Water Resources Association, Vol. 34, No. 1, pp. 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
  11. Bloschl, G., Reszler, C., and Komma, J. (2008). “A spatially distributed flash flood forecasting model.” Environmental Modelling & Software, Vol. 23, pp. 464-478. https://doi.org/10.1016/j.envsoft.2007.06.010
  12. Clark, M.P., Rupp, D.E., Woods, R.A., Zheng, X., Ibbitt, R.P., Slater, A.G., Schmidt, J., and Uddstrom, M.J. (2008). “Hydrological data assimilation with theensemble Kalman filter: use of streamflow observation to update states in a distributed hydrological model.” Advances in Water Resources, Vol. 31, pp. 1309-1324. https://doi.org/10.1016/j.advwatres.2008.06.005
  13. Evensen, G. (1992). “Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model.” Journal of Geophysical Research-Oceans, Vol. 97, No. C11, pp. 17905-17924. https://doi.org/10.1029/92JC01972
  14. Evensen, G. (1994). “Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.” Journal of Geophysical Research, Vol. 99, No. C5, pp. 10143-10162. https://doi.org/10.1029/94JC00572
  15. Georgakakos, K.P. (2008). Formulation of a system for flood forecasting in Korea based on the storage function method and distributed filtering technique. HRC Technical Note, No. 32.
  16. Kalman, R. (1960). “New approach to linear filtering and prediction problems.” Trans AMSE, Journal of Basic Engineering, Vol. 82D, pp. 35-45.
  17. Komma, J., Bloschl, G., and Reszler, C. (2008). “Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting.” Journal of Hydrology, Vol. 357, pp. 228-242. https://doi.org/10.1016/j.jhydrol.2008.05.020
  18. Liu, N., and Oliver, D.S. (2005). “Ensemble Kalman filter for automatic history matching of geologic facies.” Journal of Petroleum Science and Engineering, Vol. 47, pp. 147-161. https://doi.org/10.1016/j.petrol.2005.03.006
  19. Maybeck, P.S. (1979). Stochastic models, estimation and control, Volume 1. Academic Press, New York.
  20. Moradkhani, H., Sorooshian, S., Gupta H.V., and Houser, P.R. (2005). “Duel state parameter estimation of hydrological models using ensemble Kalman filter.” Advances in Water Resources, Vol. 28, pp. 135-147. https://doi.org/10.1016/j.advwatres.2004.09.002
  21. Neal, J.C., Atkinson, P.M., and Hutton, C.W. (2007). “Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements.” Journal of Hydrology, Vol. 336, pp. 401-415. https://doi.org/10.1016/j.jhydrol.2007.01.012
  22. Reszler, C., Komma, J., Bloschl, G., and Gutknecht, D. (2006). “An approach to identifying spatially distributed runoff models for flood forecasting.” Hydrology and Wasserbewirtschaftung, Vol. 50, No. 5, pp. 220-232.
  23. Sloan, P.G., and Moore, I.D. (1984). “Modeling subsurface stormflow on steeply sloping forested watersheds.” Water Resources Research, Vol. 20, No. 12, pp. 1815-1822. https://doi.org/10.1029/WR020i012p01815
  24. Sloan, P.G., Morre, I.D., Coltharp, G.B., and Eigel, J.D. (1983). Modeling surface and subsurface stormflow on steeply-sloping forested watersheds. Water Resources Institute Report 142. University of Kentucky, Lexington.
  25. Soil Conservation Service (1972). National Engineering Handbook: section 4 - Hydrology. SCS.
  26. Wagerner T., Boyle, D.P., Lees, M.J., Wheater, H.S., Gupta, H.V., and Sorooshian, S. (2001). “A framework for development and application of hydrological models.” Hydrology and Earth System Sciences, Vol. 5, No. 1, pp. 13-26. https://doi.org/10.5194/hess-5-13-2001
  27. Young, P.C. (2002). “Advances in real-time flood forecasting.” Philosophical Transactions of the Royal Society of London, Vol. 360, pp. 1433-1450. https://doi.org/10.1098/rsta.2002.1008

피인용 문헌

  1. Development of Realtime Dam's Hydrologic Variables Prediction Model using Observed Data Assimilation and Reservoir Operation Techniques vol.46, pp.7, 2013, https://doi.org/10.3741/JKWRA.2013.46.7.755
  2. Streamflow Forecast Model on Nakdong River Basin vol.44, pp.11, 2011, https://doi.org/10.3741/JKWRA.2011.44.11.853