Regionalization of CN Parameters for Nakdong River Basin using SCE-UA Algorithm

SCE-UA 최적화기법에 의한 낙동강 유역의 CN값 도출

  • Jeon, Ji-Hong (Department of Environmental Engineering, Andong National University) ;
  • Choi, Dong Hyuk (Department of Environmental Engineering, Andong National University) ;
  • Kim, Jung-Jin (Department of Environmental Engineering, Andong National University) ;
  • Kim, Tae Dong (Department of Environmental Engineering, Andong National University)
  • Received : 2008.12.19
  • Accepted : 2009.01.24
  • Published : 2009.03.30

Abstract

CN values are changed by various surface condition, which is cover type or treatment, hydrologic condition, or percent impervious area, even the same combination of land use and hydrologic soil group. In this study, CN parameters were regionalized for Nakdong River Basin by Long-Term Hydrologic Impact Assessment (L-THIA) coupled with SCE-UA, which is one of the global optimization technique. Six watersheds were selected for calibration (optimization) and periodic validation and two watersheds for spatical validation as ungauged watershed within Nakdong River Basin. Nash-Sutcliffe (NS) values were 0.66~0.86 for calibration, 0.68~0.91 for validation, and 0.60 and 0.85 for ungauged watersheds, respectively. Urban area for the selected watersheds covered high impervious area with 85% for residential area and 92% for commercial/industrial/transportation area. Hydrologic characteristics for crop area was similar to row crop with contoured treatment and poor hydrologic condition. For the forested area, hydrologic characteristics could be clearly distinguished from the leaf types of plant. Deciduous, coniferous, and mixed forest showed low, moderate, and high runoff rates by representing wood with fair and poor hydrologic condition, and wood-grass combination with fair hydrologic condition, respectively. CN parameters from this study could be strongly recommended to be used to simulate runoff for ungauged watershed.

Keywords

References

  1. 기상청(2007). http://kma.go.kr/gw.jsp?to=/weather_main.jsp
  2. 김종건, 임경재, 박윤식, 허성구, 박준호, 안재훈, 김기성, 최중대(2007). 경사도에 른 CN보정으로 L-THIA 직접유출모의 영향 평가. 학술발표회논문집, 한국수자원학회, pp. 1558-1562
  3. 박승우(1997). 논의 유출곡선번호의 추정과 그 적용에 관한 연구. 과학기술부 951-0601-002-2. 서울대학교
  4. 오경두, 전병호, 양경규, 안원식, 조영호(2005). 도시유역 CN 산정연구. 한국수자원학회논문집, 38(12), pp. 1009-1020 https://doi.org/10.3741/JKWRA.2005.38.12.1009
  5. Ajami, N. K., Gupta, H., Wagener, T., and Sorooshian, S. (2004). Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology, 298, pp. 112-135 https://doi.org/10.1016/j.jhydrol.2004.03.033
  6. Arnold, J. G. and Allen, P. M. (1999). Validation of Aumomated Methods for Estimating Baseflow and Groundwater Recharge from Stream Flow Records. Journal of American Water Resources Association, 35(2), pp. 411-424 https://doi.org/10.1111/j.1752-1688.1999.tb03599.x
  7. Cheng, C. T., Ou, C. P., and Chau, K. W. (2002). Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall model calibration. Journal of Hydrology, 268(1-4), pp. 72-86 https://doi.org/10.1016/S0022-1694(02)00122-1
  8. Duan, Q., Gupta, V. K., and Sorooshian, S. (1993). A shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimzation Theory Application, 76(3), pp. 501-521 https://doi.org/10.1007/BF00939380
  9. Eckhardt, K. and Arnold, J. G. (2001). Automatic calibration of distributed catchment model. Journal of Hydrology, 251, pp. 103-109 https://doi.org/10.1016/S0022-1694(01)00429-2
  10. Eckhardt, K. (2005). How to Construct Recursive Digital Filters for Baseflow Separation. Hydrological Processes, 19(2), pp. 507-515 https://doi.org/10.1002/hyp.5675
  11. Grunwald, S. and Norton, L. D. (2000). Calibration and validation of a non-point source pollution model. Agricultural Water Management, 45(1), pp. 17-39 https://doi.org/10.1016/S0378-3774(99)00074-8
  12. Jeon, J. H., Engel, B. A., Lim, K. J., and Yoon, C. G. (2007). Effect of land use type and hydrologic soil group on SCS CN uncertainty using Monte Carlo simulation. 7th International IWA Symposium on Systems Analysis and Integrated Assessment in Water Management, 7-9 May, 2007, Washington D.C., USA
  13. Lim, K. J., Engel, B. A., Tang, Z., Choi, J., Kim, K., Muthukrishnan, S., and Tripathy, D. (2005). Automated Web GIS-based Hydrograph Analysis Tool, WHAT. Journal of the American Water Resource Association, 41(6), pp. 1407- 1416 https://doi.org/10.1111/j.1752-1688.2005.tb03808.x
  14. Lim, K. J., Engel, B. A., Tang, Z., Muthukrishnan, S., Choi, J., and Kim, K. (2006). Effect of calibration on L-THIA GIS runoff and pollutant estimation. Journal of Environmental Management, 78(1), pp. 35-43 https://doi.org/10.1016/j.jenvman.2005.03.014
  15. Madsen, H. (2003). Parameter estimation in distributed hydrological catchment modeling using automatic calibration with multiple objectives. Advances in Water Researches, 26, pp. 205-216 https://doi.org/10.1016/S0309-1708(02)00092-1
  16. Mohammed, H., Yohannes, F., and Zeleke, G. (2004). Validation of agricultural non-point source (AGNPS) pollution model in Kori watershed, South Wollo, Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 6(2), pp. 97-109 https://doi.org/10.1016/j.jag.2004.08.002
  17. Muleta, M. K. and Nicklow, J. W. (2005). Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. Journal of Hydrology, 306 (1-4), pp. 127-145 https://doi.org/10.1016/j.jhydrol.2004.09.005
  18. Nash, J. E. and Sutcliffe, J. V. (1970). River flow forecasting through conceptual model. Part 1: A discussionof principles. Journal of Hydrology, 10(3), pp. 282-290 https://doi.org/10.1016/0022-1694(70)90255-6
  19. Parajka, J., Merz, R., and Bl$\ddot{o}$shl, G. (2005). A comparison of regionalization methods for catchment model parameters. Hydrology and Earth System Science, 9, pp. 157-171 https://doi.org/10.5194/hess-9-157-2005
  20. Shoemaker, L., Lahlou, M., Bryer, M., Kumar, D., and Kratt, K. (1997). Compendium of tools for watershed assessment and TMDL development, U.S. EPA, Office of Water Washinton, DC 20460, EPA841-B-97-006
  21. Web GIS-based Hydrograph Analysis Tool (WHAT) (2007). http://pasture.ecn.purdue.edu/~what