LH-OAT 민감도 분석과 SCE-UA 최적화 방법을 이용한 SWAT 모형의 자동보정 Automatic Calibration of SWAT Model Using LH-OAT Sensitivity Analysis and SCE-UA Optimization Method원문보기
본 연구에서는 LH-OAT (Latin Hypercube Ore factor At a Time) 민감도분석 방법과 SCE-UA (Shuffled Complex Evolution at University of Arizona) 최적화 기법을 적용하여 보청천 유역에서 SWAT모형에 대한 자동보정 방법을 제시하였다. LH-OAT 방법은 전역 민감도분석과 부분 민감도 분석의 장점을 조합하여 가용매개변수 공간에 대하여 효율적으로 매개변수의 민감도 분석이 가능하게 하였다. LH-OAT민감도 분석으로부터 결정된 매개변수의 민감도 등급은 SWAT 모형의 자동보정 과정에서 요구되는 보정대상 매개변수의 선택에 유용하게 적용될 수 있다. SCE-UA 방법을 적용한 SWAT모형의 자동보정 해석결과는 보정자료, 보정매개변수, 통계적 오차의 선택에 따라서 모형의 성능이 좌우되었다. 보정기간과 보정매개변수가 증가함에 따라 검증기간에 대한 RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), NMSE (Normalized Mean Square Error) 등의 모형오차는 감소하였지만, NAE (Normalized Average Error) 및 SDR(Standard Deviation Ratio)은 개선되지 않았다. SWAT모형의 보정에 적용되는 보정자료, 보정매개변수 및 모형평가를 위한 통계적 오차 선택이 해석결과에 미치는 복잡한 영향을 이해하기 위하여 다양한 대표유역을 대상으로 추가적인 연구가 필요하다.
본 연구에서는 LH-OAT (Latin Hypercube Ore factor At a Time) 민감도분석 방법과 SCE-UA (Shuffled Complex Evolution at University of Arizona) 최적화 기법을 적용하여 보청천 유역에서 SWAT모형에 대한 자동보정 방법을 제시하였다. LH-OAT 방법은 전역 민감도분석과 부분 민감도 분석의 장점을 조합하여 가용매개변수 공간에 대하여 효율적으로 매개변수의 민감도 분석이 가능하게 하였다. LH-OAT민감도 분석으로부터 결정된 매개변수의 민감도 등급은 SWAT 모형의 자동보정 과정에서 요구되는 보정대상 매개변수의 선택에 유용하게 적용될 수 있다. SCE-UA 방법을 적용한 SWAT모형의 자동보정 해석결과는 보정자료, 보정매개변수, 통계적 오차의 선택에 따라서 모형의 성능이 좌우되었다. 보정기간과 보정매개변수가 증가함에 따라 검증기간에 대한 RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), NMSE (Normalized Mean Square Error) 등의 모형오차는 감소하였지만, NAE (Normalized Average Error) 및 SDR(Standard Deviation Ratio)은 개선되지 않았다. SWAT모형의 보정에 적용되는 보정자료, 보정매개변수 및 모형평가를 위한 통계적 오차 선택이 해석결과에 미치는 복잡한 영향을 이해하기 위하여 다양한 대표유역을 대상으로 추가적인 연구가 필요하다.
The LH-OAT (Latin Hypercube One factor At a Time) method for sensitivity analysis and SCE-UA (Shuffled Complex Evolution at University of Arizona) optimization method were applied for the automatic calibration of SWAT model in Bocheong-cheon watershed. The LH-OAT method which combines the advantages...
The LH-OAT (Latin Hypercube One factor At a Time) method for sensitivity analysis and SCE-UA (Shuffled Complex Evolution at University of Arizona) optimization method were applied for the automatic calibration of SWAT model in Bocheong-cheon watershed. The LH-OAT method which combines the advantages of global and local sensitivity analysis effectively identified the sensitivity ranking for the parameters of SWAT model over feasible parameter space. Use of this information allows us to select the calibrated parameters for the automatic calibration process. The performance of the automatic calibration of SWAT model using SCE-UA method depends on the length of calibration period, the number of calibrated parameters, and the selection of statistical error criteria. The performance of SWAT model in terms of RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), and NMSE (Normalized Mean Square Error) becomes better as the calibration period and the number of parameters defined in the automatic calibration process increase. However, NAE (Normalized Average Error) and SDR (Standard Deviation Ratio) were not improved although the calibration period and the number of calibrated parameters are increased. The result suggests that there are complex interactions among the calibration data, the calibrated parameters, and the model error criteria and a need for further study to understand these complex interactions at various representative watersheds.
The LH-OAT (Latin Hypercube One factor At a Time) method for sensitivity analysis and SCE-UA (Shuffled Complex Evolution at University of Arizona) optimization method were applied for the automatic calibration of SWAT model in Bocheong-cheon watershed. The LH-OAT method which combines the advantages of global and local sensitivity analysis effectively identified the sensitivity ranking for the parameters of SWAT model over feasible parameter space. Use of this information allows us to select the calibrated parameters for the automatic calibration process. The performance of the automatic calibration of SWAT model using SCE-UA method depends on the length of calibration period, the number of calibrated parameters, and the selection of statistical error criteria. The performance of SWAT model in terms of RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), and NMSE (Normalized Mean Square Error) becomes better as the calibration period and the number of parameters defined in the automatic calibration process increase. However, NAE (Normalized Average Error) and SDR (Standard Deviation Ratio) were not improved although the calibration period and the number of calibrated parameters are increased. The result suggests that there are complex interactions among the calibration data, the calibrated parameters, and the model error criteria and a need for further study to understand these complex interactions at various representative watersheds.
Duan, Q., Sorooshian, S., and Gupta, V.K (1992). 'Effective and efficient global optimization for conceptual rainfall-runoff models.' Water Resources Research, AGU, 28(4), pp. 1015-1031
Duan, Q., Sorooshian, S., and Gupta, V.K. (1994). 'Optimal use of the SCE-UA global optimization method for calibrating watershed models.' Journal of Hydrology, ELSEVIER, 158, pp. 265-284
Gan, T.Y, and Biftu, G.F. (1996). 'Automatic calibration of conceptual rainfall-runoff models: Optimization algorithms, catchment conditions and model structure.' Water Resources Research, AGU, 32(12), pp. 3513-3524
Kim, C.G., Kim, H.J., Jang, C.B., and Kim, N.W. (2003). 'Runoff estimation from two midsize watersheds using SWAT model.' Water Engineering Research, KWRA, 4(4), pp. 193-202
Lee, D.H, Kim, N.W., and Kim, I.H (2004). 'Simulation of daily runoff and sensitivity analysis with Soil and Water Assessment Tool.' Water Engineering Research, KWRA, 5(3), pp. 133-146
McKay, M.D., Beckman, R.J. and Conover, W.J. (1979). 'A comparison of three methods for selecting values of input variables in the analysis of output from a computer code.' Technometrics, ASA, 21(2), pp. 239-245
Morris, M. D. (1991). 'Factorial sampling plans for preliminary computational experiments.' Techno. metrics, ASA, 33(2), pp, 161-174
Madsen, H. (2000). 'Automatic calibration of a conceptual rainfall-runoff model using multiple objectives.' Journal of Hydrology, ELSEVIER, 235, pp. 276- 288
Nash, J. E. and Sutcliffe, J.V, (1970). 'River flow forecasting through conceptual models: Part 1. A discussion of principles.' Journal of Hydrology, ELSEVIER, 10(3), pp. 282-290
Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams, J.R. (2001). Soil and Water Assessment Tool Version 2000, Agricultural Research Service. Texas Agricultural Experiment Station. Temple, Texas
Saxton, K.E., Rawls, W.J., Romberger, J.S., Papenclick, R.I. (1986). 'Estimating generalized soil-water characteristics from texture.' Soil Science Society of America Journal, SSSA, 55, pp. 1031-1036
Sorooshian, S., Duan, Q., and Gupta, V.K. (1993). 'Calibration of rainfall-runoff models: application of global optimization to the Sacramento soil moisture model.' Water Resources Research, AGU, 9(4), pp. 1185-1894
van Griensven, A. Francos, A. and Bauwens, W. (2002). 'Sensitivity analysis and auto-calibration of an integral dynamic model for river water quality.' Water Science and Technology, IWA, 45, pp. 325-332
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