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Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis
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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Metadata
- Authors
- Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., & Gupta, H. V.
- Venue
- Hydrological Processes
- Publisher
- Wiley
- Published
- 2003-01-23
- Volume
- 17
- Issue
- 2
- Pages
- 455-476
- ISSN
- 0885-6087 (ISSN-L)
- 1099-1085
- OpenAlex
- W2115407788
- Cited by
- 591
- References
- 0
- FWCI
- 18.85
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., & Gupta, H. V. (2003). Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis. Hydrological Processes. https://doi.org/10.1002/hyp.1135
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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