Calibration of hydrological models on hydrologically unusual events

Advances in Water Resources

Volumn 38, Issue , 2012, Pages 81-91

  Singh, Shailesh Kumar ^a   Bárdossy, András ^b  

^a NATIONAL INSTITUTE OF WATER AND ATMOSPHERIC RESEARCH   (New Zealand)

^b UNIVERSITY OF STUTTGART   (Germany)

Author keywords

Conceptual hydrological model; Critical events; Data depth function; HBV; HYMOD; ICE algorithm

Indexed keywords

CRITICAL EVENTS; DATA DEPTH; HBV; HYDROLOGICAL MODELS; HYMOD;

CATCHMENTS;

CALIBRATION;

ALGORITHM; CALIBRATION; CATCHMENT; CONCEPTUAL FRAMEWORK; DEPTH DETERMINATION; DISCHARGE; HYDROLOGICAL MODELING; MONITORING; PRECIPITATION QUALITY;

GERMANY;

HEPATITIS B VIRUS;

EID: 84862828410     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2011.12.006     Document Type: Article

References (29)

1. Seibert J. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrol Earth Syst Sci 2000, 4(2):215-224.
2. Beven K. Rainfall-runoff modelling: the primer 2001, John Wiley and Sons, Chichester.
3. Singh SK. Robust parameter estimation in gauged and ungauged basins. Transactions of the institute of hydraulic engineering, University of Stuttgart, Faculty of Civil Engineering, Stuttgart, Ph.D. dissertation No. 198, 2010.
4. Wagener T., McIntyre N., Lees M.J., Wheater H.S., Gupta H.V. Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis. Hydrol Process 2003, 17(2):455-476.
5. Gupta V.K., Sorooshian S. The relationship between data and the precision of parameter estimates of hydrologic models. J Hydrol 1985, 81:57-77.
6. Harlin J. Development of a process oriented calibration scheme for the HBV hydrological model. Nordic Hydrol 1991, 22(1):15-36.
7. Yapo P., Gupta H., Sorooshian S. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J Hydrol 1996, 181:23-48.
8. Xia Y, Yang Z-L, Jackson C, Stoffa PL, Sen MK. Impacts of data length on optimal parameter and uncertainty estimation of a land surface model, J. Geophys. Res. 109, URL http://dx.doi.org/10.1029/2003JD004419.
9. Perrin C., Oudin L., AndrTassian V., Rojas-Serna C., Michel C., Mathevet T. Impact of limited streamflow knowledge on the efficiency and the parameters of rainfall-runoff models. Hydrol Sci J 2007, 52(1):131-151.
10. Matott L.S., Rabideau A.J. Calibration of complex subsurface reaction models using a surrogate-model approach. Adv Water Resour 2008, 31(12):1697-1707.
11. Seibert J., Beven K.J. Gauging the ungauged basin: how many discharge measurements are needed?. Hydrol Earth Syst Sci 2009, 13(6):883-892. URL http://www.hydrol-earth-syst-sci.net/13/883/2009/.
12. Singh S.K., Bárdossy A. Identification of critical time periods for the efficient calibration of hydrological models. Geophys Res Abstr 2009, 12. EGU2009-5748.
13. Linsley R., Kohler M., Paulhus J. Hydrology for engineers 1949, McGraw-Hill, New York.
14. Tukey J. Mathematics and picturing data. In: Proceedings of the 1975 International 17 Congress of Mathematics 1975;2: 523-31.
15. Rousseeuw P.J., Struyf A. Computing location depth and regression depth in higher dimensions. Stat Comput 1998, 8:193-203.
16. Liu R.Y., Parelius J.M., Singh K. Multivariate analysis by data depth: descriptive statistics, graphics and inference. The Ann Stat 1999, 27(3):783-858.
17. Zuo Y., Serfling R. General notions of statistical depth function. The Ann Stat 2000, 28(2):461-482.
18. Bárdossy A., Singh S.K. Robust estimation of hydrological model parameters. Hydrol Earth Syst Sci 2008, 12(6):1273-1283. URL http://www.hydrol-earth-syst-sci.net/12/1273/2008/.
19. Samaniego L. Hydrological consequences of land use/land cover change in mesoscale catchments., Transactions of the institute of hydraulic engineering, University of Stuttgart, Faculty of Civil Engineering, Stuttgart, Ph.D. dissertation No. 118, 2003.
20. Ahmed S., de Marsily G. Comparison of geostatistical methods for estimating transmissivity using data transmissivity and specific capacity. Water Resour Res 1987, 23:1717-1737.
21. Bárdossy A., Pegram G.S., Samaniego L. Modeling data relationships with a local variance reducing technique: Applications in hydrology. Water Resour Res 2005, 41(W08404). 10.1029/2004WR003851.
22. Bergström S. The HBV model. Computer models of watershed hydrology 1995, Water Resources Publications, Littleton, Colorado, USA.
23. Hundecha Y., Bárdossy A. Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model. J Hydrol 2004, 292:281-295.
24. Moore R.J. The probability-distributed principle and runoff production at point and basin scales. Hydrol Sci J 1985, 30(2):273-297.
25. Wagener T., Boyle D.P., Lees M.J., Wheater H.S., Gupta H.V., Sorooshian S. A framework for development and application of hydrological models. Hydrol Earth Syst Sci 2001, 5(1):13-26.
26. Boyle D.P., Gupta H.V., Sorooshian S., Koren V., Zhang Z., Smith M. Toward Improved Streamflow Forecasts: Value of Semidistributed Modeling. Water Resour Res 2001, 37(11):2749-2759.
27. Singh SK, Liang J, Bárdossy A. Improving calibration strategy of physically based model WaSiM-ETH using critical events. Hydrological Sciences Journal in review.
28. Singh S.K., Hreinsson E.O., Woods R., McMillan H., Bárdossy A. Improving calibration strategy of physically based model by using critical events. Geophys Res Abstract 2010, 13. EGU2011-5641.
29. Singh SK, Jain SK, Bárdossy A. Improving efficiency in training of artificial neural networks using information-rich data. Journal of Hydroinformatics in review.