Automatic calibration of a conceptual rainfall-runoff model using multiple objectives

Journal of Hydrology

Volumn 235, Issue 3-4, 2000, Pages 276-288

  Madsen, H ^a  

^a DHI   (Denmark)

Author keywords

Calibration; Multiple objectives; Optimisation; Parameter estimation; Rainfall runoff models

Indexed keywords

ALGORITHMS; CALIBRATION; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; PARETO PRINCIPLE; RAIN; RUNOFF;

HYDROGRAPHS;

HYDROLOGY;

CALIBRATION; HYDROGRAPH; RAIN; RUNOFF MODELING;

CALIBRATION; HYDROGRAPH; RAINFALL-RUNOFF MODELING;

EID: 0034739246     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0022-1694(00)00279-1     Document Type: Article

References (31)

1. Bergström S. The HBV model. Singh V.P. Computer Models of Watershed Hydrology. 1995;443-476 Water Resources Publications, Colorado.
2. Beven K.J. Prophesy, reality and uncertainty in distributed hydrological modelling. Adv. Water Resour. 16:1993;41-51.
3. Beven K.J., Binley A.M. The future of distributed models: model calibration and uncertainty prediction. Hydrol. Process. 6:1992;279-298.
4. Burnash R.J.C. The NWS river forecast system - catchment modeling. Singh V.P. Computer Models of Watershed Hydrology. 1995;311-366 Water Resources Publications, Colorado.
5. Cooper V.A., Nguyen V.T.V., Nicell J.A. Evaluation of global optimization methods for conceptual rainfall-runoff model calibration. Water Sci. Technol. 36:(5):1997;53-60.
6. Duan Q., Sorooshian S., Gupta V. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res. 28:(4):1992;1015-1031.
7. Duan Q., Sorooshian S., Gupta V.K. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158:1994;265-284.
8. Franchini M., Galeati G., Berra S. Global optimization techniques for the calibration of conceptual rainfall-runoff models. Hydrol. Sci. J. 43:(3):1998;443-458.
9. Gan T.Y., Biftu G.F. Automatic calibration of conceptual rainfall-runoff models: optimization algorithms, catchment conditions, and model structure. Water Resour. Res. 32:(12):1996;3513-3524.
10. Gan T.Y., Dlamini E.M., Biftu G.F. Effects of model complexity and structure, data quality, and objective functions on hydrological modeling. J. Hydrol. 192:1997;81-103.
11. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning. 1989;Addison-Wesley, Reading, MA.
12. Gupta H.V., Sorooshian S., Yapo P.O. Toward improved calibration of hydrological models: multiple and noncommensurable measures of information. Water Resour. Res. 34:(4):1998;751-763.
13. Harlin J. Development of a process oriented calibration scheme for the HBV hydrological model. Nordic Hydrol. 22:1991;15-36.
14. Havnø K., Madsen M.N., Dørge J. MIKE 11 - a generalized river modelling package. Singh V.P. Computer Models of Watershed Hydrology. 1995;733-782 Water Resources Publications, Colorado.
15. Kuczera G. Efficient subspace probabilistic parameter optimization for catchment models. Water Resour. Res. 33:(1):1997;177-185.
16. Lindström G. A simple automatic calibration routine for the HBV model. Nordic Hydrol. 28:(3):1997;153-168.
17. Liong, S.Y., Khu, S.T., Chan, W.T., 1996. Construction of multi-objective function response surface with genetic algorithm and neural network. In: Proceedings of the International Conference on Water Resources and Environmental Research, 29-31 October, Kyoto, Japan, vol. II, pp. 31-38.
18. Liong S.Y., Khu S.T., Chan W.T. Derivation of Pareto front with accelerated convergence genetic algorithm, ACGA. Babovic V., Larsen L.C. Hydroinformatics'98. 1998;889-896 Balkema, Rotterdam, The Netherlands.
19. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models. Part 1: A discussion of principles. J. Hydrol. 10:1970;282-290.
20. Nelder J.A., Mead R. A simplex method for function minimization. Comput. J. 7:(4):1965;308-313.
21. Nielsen S.A., Hansen E. Numerical simulation of the rainfall runoff process on a daily basis. Nordic Hydrol. 4:1973;171-190.
22. Refsgaard J.C., Storm B. Construction, calibration and validation of hydrological models. Abbott M.B., Refsgaard J.C. Distributed Hydrological Modelling. 1996;41-54 Kluwer Academic Press, The Netherlands.
23. Sorooshian S., Gupta V.K. Model calibration. Singh V.P. Computer Models of Watershed Hydrology. 1995;23-68 Water Resources Publications, Colorado.
24. Sorooshian S., Duan Q., Gupta V.K. Calibration of rainfall-runoff models: application of global optimization to the Sacramento soil moisture accounting model. Water Resour. Res. 29:(4):1993;1185-1194.
25. Sugawara M. Tank model. Singh V.P. Computer Models of Watershed Hydrology. 1995;165-214 Water Resources Publications, Colorado.
26. Tanakamaru, H., Burges, S.J., 1996. Application of global optimization to parameter estimation of the Tank model. In: Proceedings of the International Conference on Water Resources and Environmental Research, 29-31 October, Kyoto Japan, vol. II, pp. 39-46.
27. Thyer M., Kuczera G., Bates B.C. Probabilistic optimisation for conceptual rainfall-runoff models: a comparison of the shuffled complex evolution and simulated annealing algorithms. Water Resour. Res. 35:(3):1999;767-773.
28. Wang Q.J. The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour. Res. 27:(9):1991;2467-2471.
29. Yapo P.O., Gupta H.V., Sorooshian S. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J. Hydrol. 181:1996;23-48.
30. Yapo P.O., Gupta H.V., Sorooshian S. Multi-objective global optimization for hydrological models. J. Hydrol. 204:1998;83-97.
31. Zhang X., Lindström G. Development of an automatic calibration scheme for the HBV hydrological model. Hydrol. Process. 11:1997;1671-1682.