Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose

Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measu...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2019-09, Vol.119, p.418-432
Main Authors: Guillaume, Joseph H.A., Jakeman, John D., Marsili-Libelli, Stefano, Asher, Michael, Brunner, Philip, Croke, Barry, Hill, Mary C., Jakeman, Anthony J., Keesman, Karel J., Razavi, Saman, Stigter, Johannes D.
Format: Article
Language:English
Subjects:
Derivative based methods
Emulation
Environment models
Environmental modeling
Hessian
Identifiability
Mathematical models
Non-uniqueness
Objective function
Parameter estimation
Parameter identification
Response surface
Structure-function relationships
Uncertainty
Uncertainty analysis
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Summary:Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes. [Display omitted] •Identifiability means the type of data allow parameter values to be distinguished.•Diagnosis involves examining equations or derivatives, visualizing response surface.•Important to know whether parameters are identifiable, and effect on analysis.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2019.07.007