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Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis
Author(s) -
Wagener T.,
McIntyre N.,
Lees M. J.,
Wheater H. S.,
Gupta H. V.
Publication year - 2003
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.1135
Subject(s) - identifiability , computer science , calibration , identification (biology) , conceptual model , uncertainty analysis , estimation theory , data mining , system identification , environmental science , machine learning , mathematics , algorithm , statistics , simulation , ecology , database , biology , measure (data warehouse)
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.