A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling

Water Resources Research

Volumn 37, Issue 4, 2001, Pages 937-947

  Bates, Bryson C ^a   Campbell, Edward P ^a  

^a CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; HYDROLOGY; MARKOV PROCESSES; MATHEMATICAL MODELS; RUNOFF;

METROPOLIS-HASTINGS ALGORITHM;

RAIN;

MARKOV CHAIN; MONTE CARLO SIMULATION; RAIN; RUNOFF MODELING;

ESTIMATION METHOD; MARKOV CHAIN; MONTE CARLO ANALYSIS; PARAMETERIZATION; RAINFALL-RUNOFF MODELING;

EID: 0034989285     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2000WR900363     Document Type: Article

References (40)

1. Improved methodology for parameter inference in nonlinear, hydrologic regression models
2. The future of distributed models: Model calibration and uncertainty prediction
3. A simple model for estimating the water yield of ungauged catchments
4. Evaluating partial areas of watershed runoff
5. Systematic procedure for evaluating partial areas of watershed runoff
6. A hydrograph-based model for estimating the water yield of ungauged catchments
7. An analysis of transformations
8. A Bayesian approach to parameter estimation and pooling in nonlinear flood event models
9. Understanding the Metropolis-Hastings algorithm
10. Effective and efficient global optimization for conceptual rainfall-runoff models
11. Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach
12. Automatic calibration of conceptual rainfall-runoff models: Optimization algorithms, catchment conditions, and model structure
13. Model determination using predictive distributions with implementation via sampling-based methods
14. Simulation run length control in the presence of an initial transient
15. Parameter optimization for watershed models
16. Improved parameter inference in catchment models, 1, evaluating parameter uncertainty
17. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm
18. Calibration of a conceptual rainfall-runoff model for flood frequency estimation by continuous simulation
19. Quasi-likelihood functions
20. The estimation of low flow characteristics and yield from small ungauged rural catchments
21. The SFB model part I - Validation of fixed model parameters
22. On the assessment of surface heat flux and evaporation using large-scale parameters
23. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
24. Stochastic parameter estimation procedures for hydrologic rainfall-runoff models: Correlated and heteroscedastic error cases
25. Calibration of a modified SFB model for twenty-five Australian catchments using simulated annealing
26. Application of global optimization to parameter estimation of the Tank Model
27. Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms
28. Markov chains for exploring posterior distributions
29. A note on Metropolis-Hastings kernels for general state spaces
30. Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure
31. Quasi-likelihood functions, generalized linear models and the Gauss-Newton method
32. Fully Bayesian reconstructions from single-photon emission computed tomography data
33. Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments