Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Improving Initial Abstraction Method of NRCS-CN for Estimating Effective Rainfall

유효우량 산정을 위한 NRCS-CN 모형의 초기손실량 산정방법 개선

  • Park, Dong-Hyeok (Dept. of Civil and Environmental Engineering, Hanyang Univ.) ;
  • Ajmal, Muhammad (Dept. of Civil and Environmental Engineering, Hanyang Univ.) ;
  • Ahn, Jae-Hyun (Dept. of Civil and Architecture Engineering, Seokyung Univ.) ;
  • Kim, Tae-Woong (Dept. of Civil and Environmental Engineering, Hanyang Univ.)
  • 박동혁 (한양대학교 대학원 건설환경공학과) ;
  • 무하마드 아즈말 (한양대학교 대학원 건설환경공학과) ;
  • 안재현 (서경대학교 공과대학 토목건축공학과) ;
  • 김태웅 (한양대학교 공학대학 건설환경플랜트공학과)
  • Received : 2015.03.08
  • Accepted : 2015.05.02
  • Published : 2015.06.30
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Abstract

In order to improve the runoff estimation accuracy of the Natural Resources Conservation Service (NRCS) curve number (CN) model, this study incorporated rainfall and maximum potential retention as contributors for initial abstraction. The modification was proposed based on 658 rank-order data of rainfall and subsequent runoff from 15 watersheds. The NRCS-CN model (M1), one of its inspired modified model (M2), and the proposed model (M3) were analyzed employing different CN approaches. Using tabulated, calculated and least squares fitted CNs ($CN_T$, $CN_C$, $CN_{LSF}$, respectively), the models' performances were evaluated based on Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS). Applications of model M1, M2, and M3, respectively exhibited watershed cumulative mean [RMSE (23.60, 18.12, 16.04), NSE (0.54, 0.73, 0.79), and PBIAS (36.54, 20.25, 12.00)]. Similarly, using CNC (for M1 and M2 model) and $CN_{LSF}$ (for M3 model), the performance of three models respectively were assessed based on watershed cumulative mean [RMSE (17.17, 15.88, 13.82), NSE (0.76, 0.80, 0.85), and PBIAS (3.06, 4.47, 0.11)]. The proposed model (M3) that linked all of the NRCS-CN variants showed more statistically significant agreement between the observed and estimated data.

본 연구는 NRCS-CN 방법이 산정하는 유출량의 정확성을 향상시키기 위하여 강우량과 최대잠재보유수량을 초기손실량 계산과정의 주요 기여인자로 고려하였으며, 우리나라 15개 유역에서 관측된 658개의 강우량과 유출량 자료를 이용하여 초기손실량의 수정모형을 제안하였다. 유효우량을 산정하는 방법으로는 NRCS-CN 방법(M1), NRCS-CN 방법에서 초기손실량계수를 감소시킨 방법(M2), 관측 강우-유출 관계를 바탕으로 본 연구에서 제안하는 방법(M3)을 적용하였다. 또한 USDA에서 제시하는 CN값($CN_T$), 관측자료로 부터 계산된 CN값($CN_C$) 그리고 최소자승법으로 추정한 CN값($CN_{LSF}$)을 각각의 방법에 적용하였다. 적용 결과는 RRMSE, NSE 그리고 PBIAS 등을 이용하여 평가되었다. 그 결과 $CN_T$를 M1, M2, M3에 적용한 경우 각 유역에서 평균적으로 [RMSE (23.60, 18.12, 16.04), NSE (0.54, 0.73, 0.79), PBIAS (36.54, 20.25, 12.00)]로 나타났다. 이와 비슷하게 $CN_C$를 M1과 M2에 적용하고, $CN_{LSF}$를 M3에 적용하였을 경우 각 유역에서 평균적으로 [RMSE (17.17, 15.88, 13.82), NSE (0.76, 0.80, 0.85), PBIAS (3.06, 4.47, 0.11)]로 나타났다. 따라서 본 연구에서 제안된 M3 방법을 사용하여 추정한 유효우량이 관측된 직접유출량과 통계학적으로 가장 가까운 값을 제공하는 것으로 나타났다.

Keywords

References

  1. Baltas, E.A., Dervos, N.A., and Mimikou, M.A. (2007). Technical Note: Determination of the SCS initial abstraction ratio in an experimental watershed in Greece. Hydrol. Earth Syst. Sci. Vol. 11, No. 6, pp. 1825-1829. https://doi.org/10.5194/hess-11-1825-2007
  2. Beck, H.E., De Jeu, R.A.M., Schellekens, J., van Dijk, A.I.J.M., and Bruijnzeel, L.A. (2009). Improving curve number based storm runoff estimates using soil moisture proxies. IEEE J. Sel. Topics Appl. Earth Observ. Vol. 2, No. 4, pp. 250-259. https://doi.org/10.1109/JSTARS.2009.2031227
  3. Chung, W.H., Wang, I.T., and Wang, R.Y. (2010). Theorybased SCS-CN method and its applications. J. Hydrol. Eng. Vol. 15, No. 12, pp. 1045-1058. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000281
  4. D'Asaro, F., and Grillone, G. (2012). Empirical investigation of curve number method parameters in the Mediterranean area. J. Hydrol. Eng. Vol. 17, No. 10, pp. 1141-1152. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000570
  5. Ebrahimian, M., Nuruddin, A.A.B., Soom, M.A B.M., Sood, A.M., and Neng, L.J. (2012). Runoff estimation in steep slope watershed with standard and slopeadjusted curve number methods. Pol. J. Environ. Stud. Vol. 21, No. 5, pp. 1191-1202.
  6. Hawkins, R.H., Hjelmfelt, A.T., and Zevenbergen, A.W. (1985). Runoff probability, storm depth, and curve numbers. J. Irri. Drain. Eng. Vol. 111, No. 4, pp. 330-340. https://doi.org/10.1061/(ASCE)0733-9437(1985)111:4(330)
  7. Hawkins, R.H., Ward, T.J., Woodward, D.E., and Van Mullem, J.A. (2009). Curve number hydrology-state of practice. The ASCE/EWRI, Curve Number Hydrology Task Committee.
  8. Kim, N.W., and Lee, J. (2008). Temporally weighted average curve number method for daily runoff simulation. Hydrol. Process. Vol. 22, No. 25, pp. 4936-4948. https://doi.org/10.1002/hyp.7116
  9. Kim, N.W., Lee, J.W., Lee, J., and Lee, J.E. (2010). SWAT application to estimate design runoff curve number for South Korean conditions. Hydrol. Process. Vol. 24, No. 15, pp. 2156-2170. https://doi.org/10.1002/hyp.7638
  10. Marquardt, D.W. (1963). An algorithm for least-squares estimation of nonlinear parameters." J. Soc. Ind. Appl. Math. Vol. 11, No. 2, pp. 431-441. https://doi.org/10.1137/0111030
  11. McCormick, B.C., and Eshleman, K.N. (2011). Assessing hydrologic change in surface-mined watersheds using the curve number method. J. Hydrol. Eng. Vol. 6, No. 7, pp. 575-584.
  12. Mishra, S.K., and Singh, V.P. (1999). Another look at SCSCN method. J. Hydrol. Eng. Vol. 4, No. 3, pp. 257-264. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:3(257)
  13. Mishra, S.K., and Singh, V.P. (2003). Soil conservation service curve number (SCS-CN) methodology. Kluwer Academic Dordrecht, The Netherlands, ISBN 1-4020-1132-6.
  14. Mishra, S.K., Singh, V.P., Sansalone, J.J., and Aravamuthan, V. (2003). A modified SCS-CN method: characterization and testing. Water Resour. Manage. Vol. 17, No. 1, pp. 37-68. https://doi.org/10.1023/A:1023099005944
  15. Mishra, S.K., Jain, M.K., and Singh, V.P. (2004). Evaluation of the SCS-CN-based model incorporating antecedent moisture.Water Resour. Manage. Vol. 18, No. 6, pp. 567-589. https://doi.org/10.1007/s11269-004-8765-1
  16. Mishra, S.K., Sahu, R.K., Eldho, T.I., and Jain, M.K. (2006). An improved Ia-S relation incorporating antecedent moisture in SCS-CN methodology. Water Resour. Manage. Vol. 20, No. 5, pp. 643-660. https://doi.org/10.1007/s11269-005-9000-4
  17. Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Binger, R.L., Harmel, R.D., and Veith, T.L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transac. of the ASABE Vol. 50, No. 3, pp. 885-900. https://doi.org/10.13031/2013.23153
  18. Ponce, V.M. (1989). Engineering Hydrology, Principles and Practices. San Diego State University, 508 San Diego.
  19. Ponce, V.M., and Hawkins, R.H. (1996). Runoff curve number: has it reached maturity? J. Hydrol. Eng. Vol. 1, No. 1, pp. 11-19. https://doi.org/10.1061/(ASCE)1084-0699(1996)1:1(11)
  20. Ritter, A., and Muoz-Carpena, R. (2013). Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J. Hydrol. Vol. 480, pp. 33-45. https://doi.org/10.1016/j.jhydrol.2012.12.004
  21. Schneider, L.E., and McCuen, R.H. (2005). Statistical guidelines for curve number generation. J. Irrig. Drain Eng. Vol. 131, No. 3, pp. 282-290. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:3(282)
  22. Shi, Z.H., Chen, L.D., Fang, N.F., Qin, D.F., and Cai, C.F. (2009). Research on the SCS-CN initial abstraction ratio using rainfall-runoff event analysis in the Three Gorges Area, China. Catena, Vol. 77, No. 1, pp. 1-7. https://doi.org/10.1016/j.catena.2008.11.006
  23. Soulis, K.X., Valiantzas, J.D., Dercas, N., and Londra, P.A. (2009). Investigation of the direct runoff generation mechanism for the analysis of the SCS-CN method applicability to a partial area experimental watershed. Hydrol. Earth Syst. Sci. Vol. 13, No. 5, pp. 605-615. https://doi.org/10.5194/hess-13-605-2009
  24. Soulis, K.X., and Valiantzas, J.D. (2012). SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds-the two-CN system approach. Hydrol. Earth Syst. Sci. Vol. 16, No. 3, pp. 1001-1015. https://doi.org/10.5194/hess-16-1001-2012
  25. Stewart, D., Canfield, E., and Hawkins, R.H. (2012). Curve number determination methods and uncertainty in hydrologic soil groups from semiarid watershed data. J. Hydrol. Eng. Vol. 17, No. 11, pp. 1180-1187. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000452
  26. Tedela, N.H., McCutcheon, S.C., Rasmussen, T.C., Hawkins, R.H., Swank, W.T., Campbell, J.L., Adams, M.B., Jackson, C.R., and Tollner, E.W. (2012). Runoff curve number for 10 small forested watersheds in the mountains of the Eastern United States. J. Hydrol. Eng. Vol. 17, No. 11, pp. 1188-1198. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000436
  27. USDA-Natural Resources Conservation Service (USDANRCS) (2004a). National Engineering Handbook, Part 630 Hydrology, Chapter 9: Hydrologic soil-cover complexes, Washington DC.
  28. USDA-Natural Resources Conservation Service (USDANRCS) (2004b). National Engineering Handbook, Part 630 Hydrology, Chapter 10: Estimation of direct runoff from storm rainfall, Washington DC.
  29. Van Liew, M., Veith, T., Bosch, D., and Arnold, J. (2007). Suitability of SWAT for the conservation effects assessment project: comparison on USDA agricultural research service watersheds. J. Hydrol. Eng. Vol. 12, No. 2, pp. 173-189. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:2(173)
  30. Woodward, D.E., Hawkins, R.H., Jiang, R., Hjelmfelt, A.T., van Mullem, J.A., and Quan, Q.D. (2003). Runoff curve number method: examination of the initial abstraction ratio. World Water & Environ. Resour. Congress, June 23-26, 2003, Philadelphia, Pennsylvania, United States, pp. 1-10.
  31. Yuan, Y., Nie, W., McCutcheon, S.C., and Taguas, E.V. (2014). Initial abstraction and curve numbers for semiarid watersheds in southeastern Arizona. Hydrol. Process. Vol. 28, No. 3, pp. 774-783. https://doi.org/10.1002/hyp.9592