Synthetic Streamflow Generation Using Autoregressive Modeling in the Upper Nakdong River Basin

  • Rubio, Christabel Jane P. (Ongju National University, Department of Civil and Environmental Engineering) ;
  • Oh, Kuk-Ryul (Kongju National University, Department of Civil and Environmental Engineering) ;
  • Ryu, Jae-H. (University of Idaho, Biological and Agricultural Engineering Department) ;
  • Jeong, Sang-Man (Kongju National University, Department of Civil and Environmental Engineering)
  • 발행 : 2010.02.28

초록

수자원의 관리 및 계획시 강우, 유출, 유량과 같이 다양한 종류에 의한 수문사상의 합성 및 분석이 요구된다. 다양한 수문사상들은 대부분 추계학적모형에 의한 해석이 필요하며, 이중 적절한 시계열모의결과를 나타낼 수 있는 자기회귀모형 적용을 시도하였다. 본 연구에서는 낙동강 상류에 위치한 안동댐과 임하댐 두 관측소의 월유출량 자료를 이용하여 최적의 자기회귀모형을 검토하였으며, 분석결과 AR(3) 모형의 매개변수($\phi_1$, $\phi_2$, and $\phi_3$)가 가장 적합한 것으로 나타났으며, 다양한 분석 및 평가결과 AR(3)모형이 효과적이고 정확한 것으로 나타났다.

The analysis and synthesis of various types of hydrologic variables such as precipitation, surface runoff, and discharge are usually required in planning and management of water resources. These hydrologic variables are mostly represented using stochastic models. One of which is the autoregressive model, that gives promising results in time series modeling. This study is an application of this model, which aimed to determine the AR model that best represents the historical monthly streamflow of the two gauging stations, namely Andong Dam and Imha Dam, both located in the upper Nakdong River Basin. AR(3) model was found to be the best model for both gauging stations. Parameters of the determined order of AR model ($\phi_1$, $\phi_2$ and $\phi_3$) were also estimated. Using several diagnostic tests, the efficiency of the determined AR(3) model was tested. These tests indicated the accuracy of the determined AR(3) model.

키워드

참고문헌

  1. Akaike, H. (1973) Maximum likelihood identification of Gaussian auto-regressive moving-average models, Biometrika, Vol. 60, pp. 255-266. https://doi.org/10.1093/biomet/60.2.255
  2. Box, G.E.P. and Jenkins, G.M. (1970) Time Series Analysis Forecasting and Control. Haden-bay Press, San Francisco, California.
  3. Box, G.E.P. and Pierce, D.A. (1970) Distribution of residual correlations in autoregressive-integrated moving average time series models, Journal of American Royal Statistical Society B, Vol. 65, pp. 1509-1526.
  4. Cryer, J.D. and Chan, K.S. (2008) Time Series Analysis with Application in R. Second Edition, Springer, New York.
  5. Maidment, D.R. (1993) Handbook of Hydrology, McGraw-Hill, Inc.
  6. Ministry of Construction and Transportation (MOCT) (2008). Water Management Information System (WAMIS): Daily Streamflow Series for Andong Dam (W.Y. 1966-2005) and Imha Dam (1966-2005) Gauging Stations. Available at:http://www.wamis.go.kr/WKW/wkw_cms_lst.aspx
  7. Salas, J.D., Delleur, J.W., Yevjevich, V. and Lane, W.L. (1980) Applied Modeling of Hydrological Time Series.2 Water Resources Publications, Littleton, Colorado.
  8. Thomas, H.A. and Fiering, M.B. (1962) Mathematical synthesis of streamflow sequences for the analysis of river basins by simulation. Design of Water Resources Systems, Edited by Mass, A. et al., Harvard University Press, Cambridge, Massachusetts, pp. 459-493.
  9. Yevjevich, V., (1963) Fluctuations of wet and dry years: Part I - Research data assembly and mathematical models. Hydrology Paper 1, Colorado State University, Fort Collins, Colorado.