Hydrological model parameterize using various automatic calibration techniques

Document Type : Original Research Article

Authors

1 Universitas Pertamina, Jakarta, Indonesia

2 National Institute of Water and Atmospheric Research, Christchurch, New Zealand

Abstract

Hydrological models are used for various water resources application. To represent hydrological processes, it need parameters that achieve a discharge simulation as close to the observed series as possible. The simulation result depends on how accurately the models parameters are calibrated. The calibration of model parameters depend on various factors, such as calibration methods and selected objective functions. In this study, some of the automatic calibration methods were investigated and a comparison was made to give better prediction. Different optimization algorithms like SCE-UA, SA, and ROPE were used to illustrate and calibrate the conceptual model HBV-IWS. The study was conducted on the Upper Neckar catchment, Germany. The results show that almost all optimization algorithms gave a very similar result, but the ROPE algorithm seems to be more robust. This is due to ROPE giving a space of parameter values after calibration, instead of a single parameter set as in other optimizations.

Keywords

Main Subjects


Addor, N. & Melsen, L.A., 2019. Legacy, rather than adequacy, drives the selection of hydrological models. Water resources research, 55, 378-390, https://doi.org/10.1029/2018WR022958.
Bárdossy, A. & Das, T., 2008. Influence of rainfall observation network on model calibration and application. Hydrology and earth system sciences, 12, 77-89.
Bárdossy, A. & Singh, S.K., 2008. Robust estimation of hydrological model parameters. Hydrology and earth system sciences, 12, 1273-1283.
Bergstrom, S. & Forsman, A., 1973. Development of a conceptual deterministic rainfall-runoff model. Nordic Hydrology, 4, 147-170.
Diskin, M.H. & Simon, E., 1977. A procedure for the selection of objective functions for hydrologic simulation models. Journal of Hydrology, 34, 129- 149.
Duan, Q.Y., Gupta, V.K. & Sorooshian, S., 1993. Shuffled complex evolution approach for effective and efficient global minimization. Journal of optimization theory and applications, 76(3), 501-521.
Duan, Q.Y., Sorooshian, S. & Gupta, V.K., 1994. Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of hydrology, 158, 265-284.
Gayathri, K.D., Ganasri, B.P. & Dwarakish, G.S., 2015. A review on hydrological models. Aquatic procedia, 4, 1001 – 1007.
Goswami, M. & O’Connor, K.M., 2007. Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall-runoff model, Hydrological sciences-Journal-des sciences hydrologiques, 52(3), 432-449.
Gupta, H.V., Sorooshian, S. & Yapo, P.O., 1999. Status of automatic calibration for hydrologic models: Comparison with Multilevel Expert Calibration. Journal of Hydrologic Engineering, 4(2), 135-143.
Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P., 1983. Optimization by simulated annealing. Science, 220, 671-680.
Krause, P., Boyle, D.P. & Bäse, F., 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in geosciences, 5, 89-97.
Lee, G., Tachikawa, Y. & Takara, K., 2006. Analysis of hydrologic model parameter characteristics using automatic global optimization method, Kyoto university, 49 B, 67-80.
Liu, R.Y., 1990. On a notion of data depth based on random simplices, Ann stat, 18(1), 405-414.
Madsen, H., Wilson, G. & Ammentorp, H.C., 2002. Comparison of different automated strategies for calibration of rainfall-runoff models. Journal of hydrology, 261, 48-59.
Nash, J.E. & Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I – A discussion principles. Journal of hydrology, 10, 282-290.
Nicolle, P., Pushpalatha, R., Perrin, C., Francois, D Thiery, D., Mathevet, T., Le Lay, M., Besson, Soubeyroux, J.M., Viel, C., Regimbeau, Andreassian, V., Maugis, P., Augeard, B. & Morice E., 2014. Benchmarking hydrological models low-flow simulation and forecasting on french Catchments. Hydrol. Earth Syst. Sci., 18, 2829-2857.
Rucker, D.F. & Ferre, T.P.A., 2005. Automated water content reconstruction of zero-offset borehole ground penetrating radar data using simulated annealing Journal of hydrology, 309, 1-16.
Singh, R., Subramanian, K. & Refsgaard, J.C., 1999 Hydrological modelling of a small watershed using MIKE SHE for irrigation planning. Agricultural water management, 41(3), 149-166.
Sumner, N.R., Fleming, P.M. & Bates, B.C., 1997 Calibration of a modified SFB model for twenty-five australian catchments using simulated annealing Journal of hydrology, 197, 166-188.
Tukey, J.W., 1975. Mathematics and the picturing data, Proceedings of the international congress mathematicians, 523-531. Yapo, P.O. & Gupta, H.V., Sorooshian, S., 1996 Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. Journal hydrology, 181, 23-48.
Yapo, P.O., Gupta, H.V. & Sorooshian, S., 1998. Multi Objective global optimization for hydrologic models Journal of hydrology, 204, 83-97.
Yu, Y., Disse, M., Yu, R., Yu, G., Sun, L., Huttner, P Rumbaur, C., 2015. Large scale hydrological modeling and decision making for agricultural water consumption and allocation in the main stem tarim river China. Water, 7, 2821- 2839.
Zhang, Y. & Post, D., 2018. How good are hydrological models for gap-filling streamflow data?. Hydrol earth syst. sci., 22, 4593-4604 https://doi.org/10.5194/hess-22-4593-2018.