A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

Water Resources Research

Volumn 39, Issue 8, 2003, Pages

  Vrugt, Jasper A ^a   Gupta, Hoshin V ^b   Bouten, Willem ^a   Sorooshian, Soroosh ^b  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNIVERSITY OF ARIZONA   (United States)

Author keywords

Automatic calibration; Hydrologic models; Markov Chain Monte Carlo; Parameter optimization; Proposal distribution; Uncertainty assessment

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; HYDROLOGY; MARKOV PROCESSES; MONTE CARLO METHODS; OPTIMIZATION; PROBABILITY DISTRIBUTIONS; SAMPLING;

POSTERIOR DISTRIBUTIONS; SAMPLING DISTRIBUTIONS;

WATER RESOURCES;

ALGORITHM; HYDROLOGICAL MODELING; MARKOV CHAIN; MONTE CARLO ANALYSIS; UNCERTAINTY ANALYSIS;

EID: 1542757127     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2002WR001642     Document Type: Article

References (46)

1. Beven, K. J., and A. M. Binley, The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Processes, 6, 279-298, 1992.
2. Box, G. E. P., and G. C. Tiao, Bayesian Inference in Statistical Analyses, Addison-Wesley-Longman, Reading, Mass., 1973.
3. Boyle, D. P., Multicriteria calibration of hydrological models, Ph.D. dissertation, Dep. of Hydrol. and Water Resour., Univ. of Ariz., Tucson, 2000.
4. Boyle, D. P., H. V. Gupta, and S. Sorooshian, Toward improved calibration of hydrological models: Combining the strengths of manual and automatic methods, Water. Resour. Res., 36(12), 3663-3674, 2000.
5. Brazil, L. E., Multilevel calibration strategy for complex hydrological simulation models, Ph.D. dissertation, 217 pp., Colo. State Univ., Fort Collins, Colo., 1988.
6. Dawdy, D. R., and T. O'Donnell, Mathematical models of catchment behavior, J. Hydraul. Div. Am. Soc. Civ. Eng., 91, 113-137, 1965.
7. Duan, Q., V. K. Gupta, and S. Sorooshian, Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015-1031, 1992.
8. Duan, Q., V. K. Gupta, and S. Sorooshian, A shuffled complex evolution approach for effective and efficient global minimization, J. Optim. Theory Appl., 76(3), 501-521, 1993.
9. Duan, Q., J. Schaake, and V. Koren, A priori estimation of land surface model parameters, in Land Surface Hydrology, Meteorology, and Climate, Water Sci. Appl. Ser., vol. 3, edited by V. Lakshmi, J. Albertson, and J. Schaake, pp. 77-94, AGU, Washington, D. C., 2001.
10. Gan, T. Y., and G. F. Biftu, Automatic calibration of conceptual rainfall-runoff models: Optimization algorithms, catchment conditions, and model structure, Water Resour. Res., 32(12), 3513-3524, 1996.
11. Gelman, A., and D. B. Rubin, Inference from iterative simulation using multiple sequences, Stat. Sci., 7, 457-472, 1992.
12. Gelman, A., J. B. Carlin, H. S. Stren, and D. B. Rubin, Bayesian Data Analysis, Chapmann and Hall, New York, 1995.
13. Geman, S., and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intel., 6, 721-741, 1984.
14. Geyer, C. J., Markov Chain Monte Carlo maximum likelihood, in Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, edited by E. M. Keramidas, pp. 156-163, Interface Found., Fairfax Station, Va., 1991.
15. Gilks, W. R., S. Richardson, and D. J. Spiegelharter, Introducing Markov Chain Monte Carlo, in Markov Chain Monte Carlo in Practice, edited by W. R. Gilks, S. Richardson, and D. J. Spiegelharter, pp. 1-19, Chapman and Hall, New York, 1995.
16. Gilks, W. R., S. Richardson, and D. Spiegelhalter (Eds.), Practical Markov Chain Monte Carlo, Chapman and Hall, New York, 1996.
17. Gilks, W., G. Roberts, and S. Sahu, Adaptive Markov Chain Monte Carlo through regeneration, J. Am. Stat. Assoc., 443(93), 1045-1054, 1998.
18. Gupta, H. V., S. Sorooshian, and P. O. Yapo, Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751-763, 1998.
19. Haario, H., E. Saksman, and J. Tamminen, Adaptive proposal distribution for random walk Metropolis algorithm, Comput. Stat., 14(3), 375-395, 1999.
20. Haario, H., E. Saksman, and J. Tamminen, An adaptive Metropolis algorithm, Bernoulli, 7(2), 223-242, 2001.
21. Hastings, W. K., Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97-109, 1970.
22. Hogue, T. S., S. Sorooshian, H. V. Gupta, A. Holz, and D. Braatz, A multistep automatic calibration scheme for river forecasting models, J. Hydrometeorol., 1, 524-542, 2000.
23. Holland, J., Adaptation in Natural and Artificial Systems, Univ. of Mich. Press, Ann Arbor, Mich., 1975.
24. Johnston, P. R., and D. Pilgrim, Parameter optimization for watershed models, Water Resour. Res., 12(3), 477-486, 1976.
25. Kass, R., and A. Raftery, Bayes factors, J. Am. Stat. Assoc., 90, 773-795, 1995.
26. Kuczera, G., Efficient subspace probablistic parameter optimization for catchment models, Water Resour. Res., 33(1), 177-185, 1997.
27. Kuczera, G., and M. Mroczkowski, Assessment of hydrological parameter uncertainty and the worth of multiresponse data, Water Resour. Res., 34(6), 1481-1489, 1998.
28. Kuczera, G., and E. Parent, Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm, J. Hydrol., 211, 69-85, 1998.
29. Luce, C. H., and T. W. Cundy, Parameter identification for a runoff model for forest roads, Water Resour. Res., 30(4), 1057-1069, 1994.
30. Mengersen, K. L., and R. L. Tweedie, Rates of convergence of the Hastings and Metropolis algorithms, Ann. Stat., 24, 101-121, 1996.
31. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equations of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1091, 1953.
32. Misirli, F., H. V. Gupta, S. Sorooshian, and M. Thiemann, Bayesian recursive estimation of parameter and output uncertainty for watershed models, in Calibration of Watershed Models, Water Sci. Appl. Ser., vol. 6, edited by Q. Duan et al., pp. 113-124, AGU, Washington, D. C., 2003.
33. Moore, R. J., The probability-distributed principle and runoff production at point and basin scales, Hydrol. Sci. J., 30(2), 273-297, 1985.
34. Neal, R., Probablistic inference using Markov Chain Monte Carlo methods, Tech. Rep. CRG-TR-93-1, Dep. of Comput. Sci., Univ. of Toronto, Toronto, Ont., Canada, 1993.
35. Price, W. L., Global optimization algorithms for a CAD workstation, J. Optim. Theory Appl., 55(1), 133-146, 1987.
36. Roberts, G. O., and R. L. Tweedie, Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms, Biometrika, 83, 95-110, 1996.
37. Schaap, M. G., F. J. Leij, and M. T. van Genuchten, Neural network analysis for hierarchical prediction of soil hydraulic properties, Soil Sci. Soc. Am. J., 62, 847-855, 1998.
38. Sorooshian, S., Q. Duan, and V. K. Gupta, Calibration of rainfall-runoff models: Application of global optimization to the Sacramento Soil Moisture accounting model, Water Resour. Res., 29, 1185-1194, 1993.
39. Tanakamaru, H., Parameter estimation for the tank model using global optimization, Trans. Jpn. Soc. Irrig. Drain. Reclam. Eng., 178, 103-112, 1995.
40. Tarantola, A., Inverse Problem Theory, Elsevier-Sci., New York, 1987.
41. Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian, Bayesian recursive parameter estimation for hydrological models, Water Resour. Res., 37(10), 2521-2535, 2001.
42. Thyer, M., G. Kuczera, and B. C. Bates, Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms, Water Resour. Res., 35(3), 767-773, 1999.
43. Troutmann, B., Errors and parameter estimation in precipitation runoff modeling: 1. Theory, Water Resour. Res., 21, 1195-1213, 1985.
44. Vrugt, J. A., and W. Bouten, Validity of first-order approximations to describe parameter uncertainty in soil hydrologic models, Soil Sci. Soc. Am. J., 66, 1740-1751, 2002.
45. Vrugt, J. A., W. Bouten, H. V. Gupta, and S. Sorooshian, Toward improved identifiability of hydrologic model parameters: The information content of experimental data, Water Resour. Res., 38(12), 1312, doi:10.1029/ 2001WR001118, 2002.
46. Wagener, T., D. P. Boyle, M. J. Lees, H. S. Wheater, H. V. Gupta, and S. Sorooshian, A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5(1), 13-26, 2001.