Issues of experimental design for comparing the performance of hydrologic models

Water Resources Research

Volumn 44, Issue 1, 2008, Pages

  Clarke, Robin T ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; ENGINEERING RESEARCH; MATHEMATICAL MODELS; PRECIPITATION (METEOROLOGY); RUNOFF; VEGETATION;

HYDROLOGIC MODELS; PRECIPITATION-RUNOFF MODELS;

WATERSHEDS;

CLIMATE CHANGE; ENGINEERING RESEARCH; MATHEMATICAL MODELS; PRECIPITATION (METEOROLOGY); RUNOFF; VEGETATION; WATERSHEDS;

CALIBRATION; COMPARATIVE STUDY; EXPERIMENTAL DESIGN; MODEL TEST; MODEL VALIDATION; RAINFALL-RUNOFF MODELING; WATERSHED;

EID: 39749166914     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2007WR005927     Document Type: Article

References (44)

1. Beven, K. (2006), A manifesto for the equifinality thesis, J. Hydrol., 320, 18-36.
2. Box, G. E. P., W. G. Hunter, and J. S. Hunter (1978), Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, John Wiley, Hoboken, N. J.
3. Cochran, W. G., and G. M. Cox (1957), Experimental Designs, 2nd ed., John Wiley, Hoboken, N. J.
4. Collischonn, W., C. E. M. Tucci, R. T. Clarke, M. D. Corbo, B. C. da Silva, B. Collischonn, D. Allasia, and A. Rolim da Paz (2007), Distributed hydrologic model for forecasting incremental flow in the Rio Paranaiba basin between Itumbiara and São Simão (in Portuguese), J. Braz. Water Resour. Assoc. (Rev. ABRH), in press.
5. Dawson, C. W., and R. L. Wilby (2001), Hydrological modeling using artificial neural networks, Prog. Phys. Geogr., 25(1), 80-108.
6. Diomede, T., A. Amengual, C. Marsigli, A. Martin, R. Romero, P. Papetti, and T. Paccagnella (2006), A meteo-hydrological model intercomparison as tool to quantify forecast uncertainty at medium-sized basin scale, Geophys. Res. Abstr., 8, 08164.
7. Duan, Q., S. Sorooshian, and V. K. Gupta (1994), Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol., 158, 265-284.
8. Duan, Q., V. Koren, P. Koch, and J. Schaake (1996), Use of NDVI and soil characteristics data for regional parameter estimation of hydrologic models, Eos Trans. AGU, 77(17), Spring Meet. Suppl., Abstract H42C-6.
9. Duan, Q., H. Gupta, S. Sorooshian, A. Rousseau, and R. Turcote (Eds.) (2003), Advances in Calibration of Watershed Models, Water Sci. Appl. Ser., vol. 6, AGU, Washington, D. C.
10. Duan, Q., et al. (2006), Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, J. Hydrol., 320, 3-17.
11. Francini, M., and M. Pacciani (1991), Comparative analysis of several conceptual rainfall-runoff models, J. Hydrol., 122, 161-219.
12. Guilhon, L. G. F., V. F. Rocha, and J. C. Moreira (2007), Project on the competition to develop models for forecasting inflows to hydroelectric installations of the National Interlinked System (in Portuguese), J. Braz. Water Resour. Assoc. (Rev. ABRH), in press.
13. Gupta, H. V., L. A. Bastidas, S. Sorooshian, W. J. Shuttleworth, and Z. L. Yang (1999), Parameter estimation of a land-surface scheme using multicriteria methods, J. Geophys. Res., 104(D16), 19,491-19,504.
14. Hsu, K.-L., H. V. Gupta, and S. Sorooshian (1995), Artificial neural network modeling of the rainfall-runoff process, Water Resour. Res., 31(10), 2517-2530.
15. Isabel, D., and J.-P. Villeneuve (1986), Importance of convergence criterion in the automatic calibration of hydrological models, Water Resour. Res., 22(10), 1367-1370.
16. John, P. W. M. (1971), Statistical Design and Analysis of Experiments, Macmillan, New York.
17. Klemes, V. (1986), Operational testing of hydrological simulation models, Hydrol. Sci. J., 31(1), 13-24.
18. Lee, W. (1975), Experimental Design and Analysis, W. H. Freeman, New York.
19. Littlewood, I. G., R. T. Clarke, W. Collischonn, and B. F. W. Croke (2007), Predicting daily streamflow using rainfall forecasts, a simple loss module and unit hydrographs: Two Brazilian catchments, Environ. Modell. Software, 22(9), 1229-1239.
20. Logue, K. M., and R. M. Freeze (1985), A comparison of rainfall-runoff modeling techniques on small upland catchments, Water Resour. Res., 21(2), 229-248.
21. McIntyre, N. R., H. Lee, H. S. Wheater, and A. R. Young (2004), Tools and approaches for evaluating uncertainty in streamflow predictions in ungauged UK catchments, in iEMSs 2004 International Congress: Complexity and Integrated Resource Management, edited by C. Pahl-Wostl et al., Int. Env. Modelling and Software Soc., Osnabrück, Germany.
22. Merz, R., and G. Bloschl (2004), Regionalization of catchment parameters, J. Hydrol., 287, 95-123.
23. Michaud, J., and S. Sorooshian (1994), Comparison of simple versus complex distributed runoff models on a midsize semiarid watershed, Water Resour. Res., 30(3), 593-605.
24. Montgomery, D. G. (1997), Design and Analysis of Experiments, 4th ed., John Wiley, Hoboken, N. J.
25. Naef, F. (1981), Can we model the rainfall-runoff process today?, Hydrol. Sci. Bull., 26(3), 281-289.
26. Refsgaard, J. C., and J. Knudsen (1996), Operational validation and intercomparison of different types of hydrological models, Water Resour. Res., 32(7), 2189-2202.
27. Ripley, B. D. (1993), Statistical aspects of neural networks, in Networks and Chaos: Statistical and Probabilistic Aspects, edited by O. E. Barndorff-Nielsen et al., pp. 40-123, Chapman and Hall, London.
28. Sakamoto, Y., M. Ishiguro, and G. Kitagawa (1986), Akaike Information Statistics, D. Reidel, Dordrecht, Netherlands.
29. Scheffé, H. (1959), The Analysis of Variance, John Wiley, Hoboken, N. J.
30. Shamseldin, A. Y. (1997), Application of a neural network technique to rainfall-runoff modeling, J. Hydrol., 199(3), 272-294.
31. Singh, V. P. (1995), Computer Models of Watershed Hydrology, Water Resour. Publ., Highlands Ranch, Colo.
32. Singh, V. P., and D. K. Frevert (2006), Watershed Models, Taylor and Francis, Philadelphia, Pa.
33. Smith, M. B., D.-J. Seo, V. I. Koren, S. M. Reed, Z. Ziya, D. Qingyun, F. Moreda, and C. Shuzheng (2004), The Distributed Model Intercomparison Project (DMIP): Motivation and experiment design, J. Hydrol., 298(1-4), 4-26.
34. Tang, Y., P. Reed, and T. Wagener (2006), How efficient and effective are evolutionary multiobjective algorithms in hydrologic model calibration?, Hydrol. Earth Syst. Sci., 10, 289-307.
35. Tang, Y., P. Reed, T. Wagener, and K. van Werkhoven (2007), Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11, 793-817.
36. Tonkin, M. J., and J. Doherty (2005), A hybrid regularized inversion methodology for highly parameterized environmental models, Water Resour. Res., 41, W10412, doi:10.1029/2005WR003995.
37. Vieux, B. E., C. Zhengtao, and A. Gaur (2004), Evaluation of a physics-based distributed hydrologic model for flood forecasting, J. Hydiol., 298(1-4), 155-177.
38. Vrugt, J. A., H. V. Gupta, L. A. Bastidas, W. Bouten, and S. Sorooshian (2003), Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water Resour. Res., 39(8), 1214, doi:10.1029/ 2002WR001746.
39. Wilcox, B. P., W. J. Rawls, D. L. Brackensick, and J. R. Wright (1990), Predicting runoff from rangeland catchments: A comparison of two models, Water Resour. Res., 26(10), 2401-2410.
40. World Meteorological Organization (WMO) (1975), Intercomparison of conceptual models used in operational forecasting, WMO Oper. Hydrol. Rep. 7, WMO Publ. 429, Geneva.
41. World Meteorological Organization (WMO) (1986), Intercomparison of models for snowmelt runoff, WMO Oper. Hydrol. Rep. 23, WMO Publ. 646, Geneva.
42. World Meteorological Organization (WMO) (1992), Simulated real-time intercomparison of hydrological models, WMO Oper. Hydrol. Rep. 38, WMO Publ. 779, Geneva.
43. Yapo, P., H. V. Gupta, and S. Sorooshian (1998), Multi-objective global optimization for hydrologie models, J. Hydrol., 204, 83-97.
44. Yates, F. (1964), Sir Ronald Fisher and the design of experiments, Biometrics, 20, 307-321.