Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model

Journal of Hydrology

Volumn 325, Issue 1-4, 2006, Pages 288-307

  Vrugt, Jasper A ^a   Gupta, Hoshin V ^b   Dekker, Stefan C ^c   Sorooshian, Soroosh ^d   Wagener, Thorsten ^e   Bouten, Willem ^f  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

^c UTRECHT UNIVERSITY   (Netherlands)

^d University of California at Irvine   (United States)

^e Department of Civil and Environmental Engineering ^*  (United States)

^f UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

Bayesian statistics; Hydrologic model; Markov Chain Monte Carlo methods; Parameter uncertainty; Rainfall runoff

Indexed keywords

HYDROLOGY; MARKOV PROCESSES; MATHEMATICAL MODELS; MOISTURE; MONTE CARLO METHODS; OPTIMIZATION; PARAMETER ESTIMATION; RAIN; RUNOFF; STOCHASTIC PROGRAMMING;

BAYESIAN STATISTICS; HYDROLOGIC MODELS; MARKOV CHAIN MONTE CARLO METHODS; PARAMETER UNCERTAINITY; RAINFALL-RUNOFF;

SOILS;

HYDROLOGY; MARKOV PROCESSES; MATHEMATICAL MODELS; MOISTURE; MONTE CARLO METHODS; OPTIMIZATION; PARAMETER ESTIMATION; RAIN; RUNOFF; SOILS; STOCHASTIC PROGRAMMING;

ALGORITHM; SOIL MOISTURE;

LEAF RIVER; MISSISSIPPI; NORTH AMERICA; UNITED STATES;

EID: 33744917802     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2005.10.041     Document Type: Article

References (34)

1. Beven K.J. Changing ideas in hydrology-the case of physically based model. J. Hydrol. 105 (1989) 157-172
2. Beven K.J., and Binley A.M. The future of distributed models: model calibration and predictive uncertainty. Hydrol. Process. 6 (1992) 279-298
3. Box G.E.P., and Cox D. An analysis of transformations. J. R. Stat. Soc. 26 (1974) 211-252
4. Boyle, D.P., 2000. Multicriteria calibration of hydrological models. PhD Dissertation, Department Hydrology Water Resources, University of Arizona, Tucson, AZ.
5. Boyle D.P., Gupta H.V., and Sorooshian S. Toward improved calibration of hydrological models: combining the strengths of manual and automatic methods. Water Resour. Res. 36 12 (2000) 3663-3674
6. Brazil L.E., and Hudlow M.D. Calibration procedures used with the national weather service forecast system. In: Haimes Y.Y., and Kindler J. (Eds). Water and Related Land Resources (1981), Pergamon, New York 457-466
7. Burnash R.J.C., Ferral R.L., and McGuire R.A. A generalized streamflow simulation system: conceptual models for digital computers (1973), Joint Federal-State River Forecast Center, Sacramento, CA
8. Duan Q., Gupta V.K., and Sorooshian S. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res. 28 (1992) 1015-1031
9. Gan T.Y., and Burges S.J. An assessment of a conceptual rainfall-runoff model's ability to represent the dynamics of small hypothetical catchments, 2: hydrologic responses for normal and extreme rainfall. Water Resour. Res. 26 7 (1990) 1605-1619
10. Gelman A., and Rubin D.B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7 (1992) 457-472
11. Gupta V.K., and Sorooshian S. The relationship between data and the precision of parameter estimates of hydrological models. J. Hydrol. 81 (1985) 57-77
12. Gupta H.V., Sorooshian S., and Yapo P.O. Towards improved calibration of hydrologic models: multiple and non-commensurable measures of information. Water Resour. Res. 34 (1998) 751-763
13. Gupta, H.V., Bastidas, L., Vrugt, J.A., Sorooshian, S., 2002. Multiple criteria global optimisation for watershed model calibration. In: Duan, Q., Gupta, H.V., Sorooshian, S., Rousseau, A.N., Turcotte R., (Eds.), AGU Monograph: Advances in Automatic Calibration of Watershed Models.
14. Hastings W.K. Monte-Carlo sampling methods using Markov Chains and their applications. Biometrika 57 (1970) 97-109
15. Hooper R.P., Stone A., Christophersen N., de Grosbois E., and Seip H.M. Assessing the Birkenes model of stream acidification using a multisignal calibration methodology. Water Resour. Res. 24 (1988) 1308-1316
16. Jakeman A., and Hornberger G.M. How much complexity is warranted in a rainfall-runoff model?. Water Resour. Res. 29 8 (1993) 2637-2649
17. Jakeman A.J., Dietrich C.R., and Thomas G.A. Solute transport in a stream aquifer system, 2, application of model identification to the river Murray. Water Resour. Res. 25 (1989) 2177-2185
18. Jakeman A.J., Littlewood I.G., and Whitehead P.G. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. J. Hydrol. 117 (1990) 275-300
19. Kitanidis P.K., and Bras R.L. Adaptive filtering through detection of isolated transient errors in rainfall-runoff models. Water Resour. Res. 16 4 (1980) 740-748
20. Kitanidis P.K., and Bras R.L. Real-time forecasting with a conceptual hydrological model, 1, analysis of uncertainty. Water Resour. Res. 16 6 (1980) 1025-1033
21. Kuczera G. Efficient subspace probabilistic parameter optimization for catchment models. Water Resour. Res. 33 1 (1997) 177-185
22. Madsen H. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J. Hydrol. 235 (2000) 276-288
23. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., and Teller E. Equations of state calculations by fast computing machines. J. Chem. Phys. 21 (1953) 1087-1091
24. Misirli, F., 2003. Improving efficiency and effectiveness of Bayesian recursive parameter estimation for hydrological models. PhD Dissertation, Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ.
25. Moore R.J. The probability-distributed principle and runoff production at point and basin scales. Hydrol. Sci. J. 30 2 (1985) 273-297
26. Peck E.L. Catchment modeling and initial parameter estimation for the national weather service river forecast system. NOAA Technology Memorandrom, NWS HYDRO-31 (1976), National Weather Service, Silver Spring, MD
27. Sorooshian S., Gupta V.K., and Fulton J.L. 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 (1983) 251-259
28. Sorooshian S., Duan Q., and Gupta V.K. Calibration of rainfall-runoff models: application of global optimization to the Sacramento Soil Moisture Accounting model. Water Resour. Res. 29 (1993) 1185-1194
29. Thiemann M., Trosset M., Gupta H., and Sorooshian S. Bayesian recursive parameter estimation for hydrological models. Water Resour. Res. 37 10 (2001) 2521-2535
30. Vrugt J.A., Gupta H.V., Bouten W., and Sorooshian S. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res. 39 8 (2003) 1201 10.1029/2002WR001642
31. Vrugt J.A., Diks C.G.H., Gupta H.V., Bouten W., and Verstraten J.M. Improved treatment of uncertainty in hydrologic modelling. combining the strengths of global optimization and data assimilation. Water Resour. Res. 41 1 (2005) W01017 10.1029/2004WR003059
32. Vrugt J.A., Robinson B.A., and Vesselinov V.V. Improved inverse modelling for transport in subsurface media: combined parameter and state estimation. Geophys. Res. Let. (2005) L18408 10.1029/2005GL023940
33. Vrugt, J.A., Ó Nualláin, B., Robinson, B.A., Bouten, W., Dekker, S.C., Sloot, P.M.A., in press. Application of parallel computing to stochastic parameter estimation in environmental models. Computers & Geosciences.
34. Yapo P.O., Gupta H.V., and Sorooshian S. Calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J. Hydrol. 181 (1996) 23-48