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 journalArticle

365 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 languageEnglish (US)
Pages (from-to)455-476
Number of pages22
JournalHydrological Processes
Volume17
Issue number2
DOIs
StatePublished - 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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