A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling

Water Resources Research

Volumn 40, Issue 2, 2004, Pages

  Marshall, Lucy ^a   Nott, David ^a   Sharma, Ashish ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Bayesian; Monte Carlo Markov chain; Rainfall runoff

Indexed keywords

CATCHMENTS; COMPUTER SIMULATION; HYDROLOGY; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; RAIN; RISK ASSESSMENT; STATISTICS; WATER RESOURCES;

HYDROLOGISTS;

RUNOFF;

ALGORITHM; COMPARATIVE STUDY; HYDROLOGICAL MODELING; MARKOV CHAIN; MONTE CARLO ANALYSIS; RAINFALL-RUNOFF MODELING; UNCERTAINTY ANALYSIS;

EID: 1642295818     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2003WR002378     Document Type: Article

References (35)

1. Bates, B. C., and E. P. Campbell (2001), A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling, Water Resour. Res., 37(4), 937-947.
2. Besag, J., and P. J. Green (1993), Spatial statistics and Bayesian computation, J. R. Stat. Soc., Ser. B, 55(1), 25-37.
3. Boughton, W. C. (1984), A simple model for estimating the water yield of ungauged catchments, Civ. Eng. Trans. Inst. Eng. Aust., 26(2), 83-88.
4. Boughton, W. C. (1993), A hydrograph based model for estimating the water yield of ungauged catchments, Natl. Conf. Publ. Inst. Eng. Aust., 93/14, 317-324.
5. Bras, R. L., and I. Rodriguez-Iturbe (1993), Random Functions and Hydrology, 559 pp., Dover, Mineola, N. Y.
6. Campbell, E. P., D. R. Fox, and B. C. Bates (1999), A Bayesian approach to parameter estimation and pooling in nonlinear flood event models, Water Resour. Res., 35(1), 211-220.
7. Chiew, F. H. S., and T. A. McMahon (1994), Application of the daily rainfall-runoff model MODHYDROLOG to 28 Australian catchments, J. Hydrol., 153, 383-416.
8. Duan, Q., S. Sorooshian, and V. K. Gupta (1992), Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28(4), 1015-1031.
9. Fiering, M. B. (1967), Streamflow Synthesis, Harvard Univ Press, Cambridge, Mass.
10. Freer, J., K. Beven, and B. Ambroise (1996), Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach, Water Resour. Res., 32(7), 2161-2173.
11. Gan, T. Y., and G. F. Biftu (1996), Automatic calibration of conceptual rainfall-runoff models: Optimization algorithms, catchment conditions, and model structures, Water Resour. Res., 52(12), 3513-3524.
12. Gelman, A., and D. B. Rubin (1992), Inference from iterative simulation using multiple sequences, Stat. Sci., 7(4), 457-511.
13. Gelman, A., J. Carlin, H. S. Stern, and D. B. Rubin (1995), Bayesian Data Analysis, 526 pp., Chapman and Hall, New York.
14. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter (1996), Markov Chain Monte Carlo in Practice, 486 pp., Chapman and Hall, New York.
15. Haario, H., E. Saksman, and J. Tamminem (2001), An adaptive Metropolis algorithm, Bernoulli, 7(2), 223-242.
16. Haario, H., E. Saksman, and J. Tamminem (2003), Componentwise adaptation for MCMC, Preprint 342, Dep. of Math., Univ. of Helsinki, Helsinki.
17. Hastings, W. K. (1970), Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97-109.
18. Kuczera, G. (1983), Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty, Water Resour. Res., 19(5), 1151-1162.
19. Kuczera, G. (1988), On the validity of first-order prediction limits for conceptual hydrologic models, J. Hydrol., 103, 229-247.
20. Kuczera, G., and M. Mroczkowski (1998), Assessment of hydrologic parameter uncertainty and the worth of multiresponse data, Water Resour. Res., 34(6), 1481-1489.
21. Kuczera, G., and E. Parent (1998), Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm, J. Hydrol., 211, 69-85.
22. Lamb, R. (1999), Calibration of a conceptual rainfall-runoff model for flood estimation by continuous simulation, Water Resour. Res., 35(10), 3103-3114.
23. Madsen, H. (2000), Automatic calibration of a conceptual rainfall-runoff model using multiple objectives, J. Hydrol., 235, 276-288.
24. Mein, R. G., and B. M. Brown (1978), Sensitivity of optimized parameters in watershed models, Water Resour. Res., 14(2), 299-303.
25. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller (1953), Equations of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1092.
26. Nash, J. E., and J. V. Sutcliffe (1970), River flow forecasting through conceptual models, 1. A discussion of principles, J. Hydrol., 10, 282-290.
27. Nathan, R. J., and T. A. McMahon (1990), The SFB model part II - Operational considerations, Civ. Eng. Trans. Inst. Eng. Aust., 32(3), 162-166.
28. Romanowicz, R., K. Beven, and J. A. Tawn (1994), Evaluation of predictive uncertainty in nonlinear hydrological models using a Bayesian approach, in Statistics for the Environment 2: Water Related Issues, edited by V. Barnett and K. F. Turkman, pp. 297-317, John Wiley, Hoboken, N. J.
29. Sivapalan, M., J. K. Ruprecht, and N. R. Viney (1996), Water and salt balance modeling to predict the effects of land use changes in forested catchments, 1, Small catchment water balance model, Hydrol. Processes, 10, 393-411.
30. Sorooshian, S., and J. A. Dracup (1980), Stochastic parameter estimation procedures for hydrologic rainfall-runoff models: Correlated and heteroscedastic error cases, Water Resour. Res., 16(2), 430-442.
31. Sorooshian, S., and V. K. Gupta (1983), Automatic calibration of conceptual rainfall-runoff models: The question of parameter observability and uniqueness, Water Resour. Res., 19(1), 260-268.
32. Sorooshian, S., V. K. Gupta, and J. L. Fulton (1983), Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runoff models: Influence of calibration data variability and length on model credibility, Water Resour. Res., 19(1), 251-259.
33. Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian (2001), Bayesian recursive parameter estimation for hydrologic models, Water Resour. Res., 37(10), 2521-2535.
34. Thyer, M., G. Kuczera, and B. C. Bates (1999), 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.
35. Yapo, P., S. Sorooshian, and V. K. Gupta (1996), Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data, J. Hydrol., 181, 23-48.