A single model ensemble versus a dynamic modeling platform: Semi-distributed rainfall runoff modeling in a Hierarchical Mixtures of Expert framework

Geophysical Research Letters

Volumn 34, Issue 1, 2007, Pages

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

^a MONTANA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CATCHMENTS; COMPUTER SIMULATION; MATHEMATICAL MODELS; PARAMETERIZATION; PROBABILITY; RUNOFF;

DYNAMIC MODELING PLATFORM; RAINFALL RUNOFF MODELING;

HYDROLOGY;

CATCHMENT; HIERARCHICAL SYSTEM; HYDROLOGICAL MODELING; PARAMETERIZATION; PROBABILITY; RAINFALL-RUNOFF MODELING; SIMULATION;

EID: 34548011007     PISSN: 00948276     EISSN: None     Source Type: Journal    
DOI: 10.1029/2006GL028054     Document Type: Article

References (17)

1. Bates, B. C., and E. Campbell (2001), A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-run-off modeling, Water Resour. Res., 37, 937-947.
2. Beven, K., and J. Freer (2001), A dynamic Topmodel, Hydrol. Processes, 15, 1993-2011.
3. Beven, K., and M. J. Kirkby (1979), A physically based variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, 43-69.
4. Beven, K., R. Lamb, P. F. Quinn, R. Romanowicz, and J. Freer (1995), Topmodel, in Computer Models of Watershed Hydrology, edited by V. P. Singh, pp. 627-668, Water Resour. Publ., Highlands Ranch, Colo.
5. Grayson, R. B., and G. Bloschl (2001), Spatial Patterns in Catchment Hydrology, 432 pp., Cambridge Univ. Press, New York.
6. Haario, H., E. Saksman, and J. Tamminen (2001), An adaptive Metropolis algorithm, Bernoulli, 7, 223-242.
7. Hastie, T., R. Tibshirani, and J. Freidman (2001), The Elements of Statistical Learning; Data Mining, Inference and Prediction, Springer, New York.
8. Jordan, M. I., and R. A. Jacobs (1994), Hierarchical mixtures of experts and the EM algorithm, Neural Comput., 6, 181-214.
9. Marshall, L., D. J. Nott, and A. Sharma (2004), A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling, Water Resour. Res., 40, W02501, doi: 10.1029/ 2003WR002378.
10. Marshall, L., A. Sharma, and D. J. Nott (2006a), Modeling the catchment via mixtures: issues of model specification and validation, Water Resour. Res., 42, W11409, doi: 10.1029/2005WR004613.
11. Marshall, L., D. J. Nott, and A. Sharma (2006b), Towards dynamic catchment modelling: A Bayesian Hierarchical Mixtures of Experts framework, Hydrol. Processes, in press.
12. Peng, F., R. A. Jacobs, and M. A. Tanner (1996), Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with an application to speech recognition, J. Am. Stat. Assoc., 91, 953-960.
13. Refsgaard, J. C., J. P. van der Sluijs, J. Brown, and R van der Keur (2006), A framework for dealing with uncertainty due to model structure error, Adv. Water Resour., 29, 1586-1597.
14. Schwarz, G. (1978), Estimating the dimension of a model, Ann. Stat., 6, 461-464.
15. Sharma, A., L. Marshall, and D. J. Nott (2006), A Bayesian view of rain-fall-ranoff modeling: Alternatives for parameter estimation, model comparison and hierarchical model development, in Prediction in Ungauged Basins: Promises and Progress, edited by M. Sivapalan et al., chap. 23, IAHS Press, Wallingford, U.K., in press.
16. Western, A. W., R. B. Grayson, and T. R. Green (1999), The Tarrawarra project: High resolution spatial measurement, modelling and analysis of soil moisture and hydrological response, Hydrol. Processes, 13, 633-652.
17. Xiong, L., and S. Guo (2004), Effects of the catchment runoff coefficient on the performance of Topmodel in rainfall-runoff modelling, Hydrol. Processes, 18, 1823-1836.