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Spatial Extension of Runoff Data in the Applications of a Lumped Concept Model

집중형 수문모형을 활용한 홍수유출자료 공간적 확장성 분석

  • Kim, Nam Won (Hydrology Research Div., Korea Institute of Construction Technology) ;
  • Jung, Yong (Hydrology Research Div., Korea Institute of Construction Technology) ;
  • Lee, Jeong Eun (Hydrology Research Div., Korea Institute of Construction Technology)
  • 김남원 (한국건설기술연구원, 수자원연구실) ;
  • 정용 (한국건설기술연구원, 수자원연구실) ;
  • 이정은 (한국건설기술연구원, 수자원연구실)
  • Received : 2013.05.10
  • Accepted : 2013.07.22
  • Published : 2013.09.30

Abstract

Runoff data availability is a substantial factor for precise flood control such as flood frequency or flood forecasting. However, runoff depths and/or peak discharges for small watersheds are rarely measured which are necessary components for hydrological analysis. To compensate for this discrepancy, a lumped concept such as a Storage Function Method (SFM) was applied for the partitioned Choongju Dam Watershed in Korea. This area was divided into 22 small watersheds for measuring the capability of spatial extension of runoff data. The chosen total number of flood events for searching parameters of SFM was 21 from 1991 to 2009. The parameters for 22 small watersheds consist of physical property based (storage coefficient: k, storage exponent: p, lag time: $T_l$) and flood event based parameters (primary runoff ratio: $f_1$, saturated rainfall: $R_{sa}$). Saturated rainfall and base flow from event based parameters were explored with respect to inflow at Choongju Dam while other parameters for each small watershed were fixed. When inflow of Choongju Dam was optimized, Youngchoon and Panwoon stations obtained average of Nash-Sutcliffe Efficiency (NSE) were 0.67 and 0.52, respectively, which are in the satisfaction condition (NSE > 0.5) for model evaluation. This result is showing the possibility of spatial data extension using a lumped concept model.

홍수빈도해석이나 홍수예측과 같은 홍수조절을 위한 필수적인 정보는 유출자료이다. 하지만, 소규모 유역의 경우 유출자료를 측정하지 않는 미계측 유역의 다지점 분석과 총량분석을 위한 정보가 너무 부족한 실정으로 이를 극복하기 위한 방안을 제시하였다. 이를 위해 본 연구에서는 집중형 모델인 저류함수법를 활용하여 충주댐유역을 세분화하여 적용하였다. 충주댐 유역은 22개의 소유역으로 분류하였으며 충주댐 수위관측소의 유출자료의 공간적 확장성을 검증하였다. 홍수사상은 1990년부터 2009년까지의 21개 홍수사상을 활용하여 한 곳(충주댐 유입량)의 자료를 중심으로 22개 소유역의 저류함수법의 수문지형학적 특성에 관여하는 매개변수(k, p, $T_l$)를 고정하고 홍수사상마다 달라지는($f_1$, $R_{sa}$)를 최적화 하며 22개 유역의 유출자료를 생산하였다. 교차검증 지점인 영춘과 판운 수위관측소의 평균 Nash-Sutcliffe Efficiency (NSE)는 충주댐의 유입량이 0.71을 나타낼 때 각각 0.67과 0.52를 나타내 유출자료의 확장성에 있어서 만족(NSE > 0.5)하는 범위에 들어 집중형 모형을 활용한 유출자료의 확장가능성을 보였다.

Keywords

References

  1. Bae, D.-H. (1997). "Development of stochastic real-time flood forecast system by storage function method." J. of Korea Water Reso. Asso., Vol. 30, No. 5, pp. 449-457.
  2. Bae, D.-H., and Chung, I.-M. (2000). "Development of stochastic-dynamic channel routing model by storage function method." J. of Korea Water Reso. Asso., Vol. 33, No. 3, pp. 341-350.
  3. Bloschl, G., and Sivapalan, M. (1995). "Scale issues in hydrological modelling-a review." Hydrol. Process. Vol. 9, pp. 251-290. https://doi.org/10.1002/hyp.3360090305
  4. Boughton, W., and Chiew, F. (2007). "Estimating runoff in ungauged catchments from rainfall, PET and the AWBM model." Environ. Model. Soft., Vol. 22, pp. 476-487. https://doi.org/10.1016/j.envsoft.2006.01.009
  5. Chapra, S.C., and Canale, R.P. (2006). Numerical methods for engineers 5th edition, McGraw Hill Higher Education, NewYork.
  6. Chung, G., Park, H.-S., Sung, J.Y., and Kim, H.-J. (2012). "Determination and evaluation of optimal parameters in storage function method using SCE-UA." J. of Korea Water Reso. Asso., Vol. 45, No. 11, pp. 1169-1186. https://doi.org/10.3741/JKWRA.2012.45.11.1169
  7. Kim, B.J., Kawk, J.W., Lee, J.H., and Kim, H.S. (2008) "Calibration and estimation of parameter for storage function model." J. of Korean Soceity of Civil Engineering, Vol. 28, No. 1b, pp. 21-32.
  8. Kim, B.J., Song, J.H., Kim, H.S., and Hong, I.P. (2006). "Parameter calibration of Storage function model and flood forecasting (2) comparative study on the flood forecasting methods." J. of Korean Soceity of Civil Engineering, Vol. 26, No. 1b, pp. 39-50.
  9. Kim, T., and Yoon, K. (2007). "Application of storage function method with SCS method." J. of Korea Water Reso. Asso., Vol. 40, No. 7, pp. 523-532. https://doi.org/10.3741/JKWRA.2007.40.7.523
  10. Kimura, T. (1961). Storage function methods for flood routing, Ph.D. dissertation, Research Institute of Japan Civil Engineering, pp. 89-96, 203-209.
  11. Lindeman, R.H., Merenda, P.F., and Gold, R.Z. (1980). Introduction to Bivariate and Multivariate Analysis. Scott, Foresman, Glenview, IL.
  12. Merz, R., and Bloschl, G. (2004). "Regionalisation of catchment model parameters." J. of Hydrology, Vol. 287, pp. 95-123. https://doi.org/10.1016/j.jhydrol.2003.09.028
  13. Ministiry of Land, Infrasturture and Transport (2001). Seomjin River Flood Forecasting and Warning Report, Research Report, Seomjin River Flood Control Office.
  14. Ministiry of Land, Infrasturture and Transport (2004). Improvement of flood forecasting and warning systeme for Keum River: Yongdam Dam and Mihochun, Research Report, Korea Institute of Construction Technology.
  15. Ministiry of Land, Infrasturture and Transport (2005a). Installation of integrated flood control system with dam operation in Han River Watershed, Research Report, K-Water.
  16. Ministiry of Land, Infrasturture and Transport (2005b). Improvement and evaluation of flood forecasting and warning model (1st Session), Research Report, Korea Institute of Construction Technology.
  17. Ministiry of Land, Infrasturture and Transport (2006). Improvement and evaluation of flood forecasting and warning model (2nd Session), Research Report, Korea Institute of Construction Technology.
  18. Moriasi, D.N., Arnold, J.G., Van Liew M.W., Bingner R.L., Hrmel, R.D., and Veith, T.L. (2007). "Model evaluation guidelines for systematic quantification of accuracy in watershed simulations." American Society of Agricultural and Biological Engineers, Vol. 50, No. 3, pp. 885-900.
  19. Nash, J.E., and Sutcliffe J.V. (1970). "River flow forecasting through conceptual models part I-A discussion of principles." J. of Hydrology, Vol. 10, No. 3, pp. 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  20. Parajka, J., Merz, R., and Bloschl, G. (2005). "A comparison of regionalization methods for catchment model parameters." Hydrol. Earth Syst. Sci., Vol. 9, pp. 157-171. https://doi.org/10.5194/hess-9-157-2005
  21. Park, B.-J, Cha, H.-S., and Kim, J.-H. (1997). "A study on parameter estimation of storate function model using the genetic algorithms." J. of Korea Water Reso. Asso., Vol. 30, No. 4, pp. 347-355.
  22. Piman, T., and Babel, M.S. (2013). "Prediction of rainfallrunoff in an ungauged basin: Case study in the mountainous region of Northern Thailand." J. of Hydrolologic Engineering, Vol. 13, No. 2, pp. 285-296.
  23. Shin, C.-K, Cho, H.-S., Jung, K.-S., and Kim, J.-H. (2004). "Grid based rainfall-runoff modeling using storage function method." J. of Korea Water Resources Association, Vol. 37, No. 11, pp. 969-978. https://doi.org/10.3741/JKWRA.2004.37.11.969
  24. Song, J.H., Kim, H.S., Hong, I.P., and Kim, S.U. (2006). "Parameter calibration of storage function model and flood forecasting (1) Clibration methods and evaluation of simulated flood hydrograph." J. of Korean Soceity of Civil Engineering, Vol. 26, No. 1b, pp. 27-38.
  25. Sugiyama, H., Kadoya, M., Nagai, A., and Lausey, K. (1997). "Evaluation of the storage function model parameter characteristics." J. of Hydrology, Vol. 191, pp. 332-348. https://doi.org/10.1016/S0022-1694(96)03026-0
  26. Wagener, T., and Wheater, H.S. (2006). "Parameter estimation and regionalization for continous rainfallrunoff models including uncertainty." J. of Hydrology, Vol. 320, pp. 132-154. https://doi.org/10.1016/j.jhydrol.2005.07.015
  27. Yoon, T.H. (2011). Applied Hydrology-Theory and Applications, Chungmoon Gak, Seoul, Korea. pp. 807.

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