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

Authors

  • T. Wagener,

    Corresponding author
    1. Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, Imperial College Road, London SW7 2BU, UK
    • NSF Center for Sustainability of Semi-Arid Hydrology and Riparian Areas (SAHRA), Department of Hydrology and Water Resources, Harshbarger Building, PO Box 210011, Tucson, Arizona 85721-0011, USA.
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  • N. McIntyre,

    1. Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, Imperial College Road, London SW7 2BU, UK
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  • M. J. Lees,

    1. Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, Imperial College Road, London SW7 2BU, UK
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  • H. S. Wheater,

    1. Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, Imperial College Road, London SW7 2BU, UK
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  • H. V. Gupta

    1. Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, USA
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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. Copyright © 2003 John Wiley & Sons, Ltd.

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