Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis

T. Wagener; N. McIntyre; M. J. Lees; H. S. Wheater; H. V. Gupta

doi:10.1002/hyp.1135

Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis

T. Wagener, N. McIntyre, M. J. Lees, H. S. Wheater, H. V. Gupta

Research output: Contribution to journal › Article › peer-review

446 Scopus citations

Abstract

Conceptual modelling requires the identification of a suitable model structure and the estimation of parameter values through calibration against observed data. A lack of objective approaches to evaluate model structures and the inability of calibration procedures to distinguish between the suitability of different parameter sets are major sources of uncertainty in current modelling procedures. This paper presents an approach analysing the performance of the model in a dynamic fashion resulting in an improved use of available information. Model structures can be evaluated with respect to the failure of individual components, and periods of high information content for specific parameters can be identified. The procedure is termed dynamic identifiability analysis (DYNIA) and is applied to a model structure built from typical conceptual components.

Original language	English (US)
Pages (from-to)	455-476
Number of pages	22
Journal	Hydrological Processes
Volume	17
Issue number	2
DOIs	https://doi.org/10.1002/hyp.1135
State	Published - Feb 15 2003

Keywords

Conceptual rainfall-runoff models
Information content of data
Model structural analysis
Parameter identifiability

ASJC Scopus subject areas

Water Science and Technology

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10.1002/hyp.1135

Cite this

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abstract = "Conceptual modelling requires the identification of a suitable model structure and the estimation of parameter values through calibration against observed data. A lack of objective approaches to evaluate model structures and the inability of calibration procedures to distinguish between the suitability of different parameter sets are major sources of uncertainty in current modelling procedures. This paper presents an approach analysing the performance of the model in a dynamic fashion resulting in an improved use of available information. Model structures can be evaluated with respect to the failure of individual components, and periods of high information content for specific parameters can be identified. The procedure is termed dynamic identifiability analysis (DYNIA) and is applied to a model structure built from typical conceptual components.",

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AU - McIntyre, N.

AU - Lees, M. J.

AU - Wheater, H. S.

AU - Gupta, H. V.

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AB - Conceptual modelling requires the identification of a suitable model structure and the estimation of parameter values through calibration against observed data. A lack of objective approaches to evaluate model structures and the inability of calibration procedures to distinguish between the suitability of different parameter sets are major sources of uncertainty in current modelling procedures. This paper presents an approach analysing the performance of the model in a dynamic fashion resulting in an improved use of available information. Model structures can be evaluated with respect to the failure of individual components, and periods of high information content for specific parameters can be identified. The procedure is termed dynamic identifiability analysis (DYNIA) and is applied to a model structure built from typical conceptual components.

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Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis

Abstract

Keywords

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