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Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology.

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Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. / Freer, Jim E.; Beven, Keith J.
In: Journal of Hydrology, Vol. 249, No. 1-4, 01.08.2001, p. 11-29.

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@article{cd371da94b7e446cb1fe6726db256fe5,
title = "Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology.",
abstract = "It may be endemic to mechanistic modelling of complex environmental systems that there are many different model structures and many different parameter sets within a chosen model structure that may be behavioural or acceptable in reproducing the observed behaviour of that system. This has been called the equifinality concept. The generalised likelihood uncertainty estimation (GLUE) methodology for model identification allowing for equifinality is described. Prediction within this methodology is a process of ensemble forecasting using a sample of parameter sets from the behavioural model space, with each sample weighted according to its likelihood measure to estimate prediction quantiles. This allows that different models may contribute to the ensemble prediction interval at different time steps and that the distributional form of the predictions may change over time. Any effects of model nonlinearity, covariation of parameter values and errors in model structure, input data or observed variables, with which the simulations are compared, are handled implicitly within this procedure. GLUE involves a number of choices that must be made explicit and can be therefore subjected to scrutiny and discussion. These include ways of combining information from different types of model evaluation or from different periods in a data assimilation context. An example application to rainfall-runoff modelling is used to illustrate the methodology, including the updating of likelihood measures.",
keywords = "TOPMODEL, Maimai catchment, Rainfall-runoff modelling, Parameter conditioning, Prediction uncertainty, GLUE",
author = "Freer, {Jim E.} and Beven, {Keith J.}",
note = "Highly cited paper (133 to Sep 07) that presents a summary of the GLUE methodology at that time. JEF carried out all the analyses on which the paper was based and provided major inputs to the papers development. RAE_import_type : Journal article RAE_uoa_type : Earth Systems and Environmental Sciences",
year = "2001",
month = aug,
day = "1",
doi = "10.1016/S0022-1694(01)00421-8",
language = "English",
volume = "249",
pages = "11--29",
journal = "Journal of Hydrology",
publisher = "Elsevier Science B.V.",
number = "1-4",

}

RIS

TY - JOUR

T1 - Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology.

AU - Freer, Jim E.

AU - Beven, Keith J.

N1 - Highly cited paper (133 to Sep 07) that presents a summary of the GLUE methodology at that time. JEF carried out all the analyses on which the paper was based and provided major inputs to the papers development. RAE_import_type : Journal article RAE_uoa_type : Earth Systems and Environmental Sciences

PY - 2001/8/1

Y1 - 2001/8/1

N2 - It may be endemic to mechanistic modelling of complex environmental systems that there are many different model structures and many different parameter sets within a chosen model structure that may be behavioural or acceptable in reproducing the observed behaviour of that system. This has been called the equifinality concept. The generalised likelihood uncertainty estimation (GLUE) methodology for model identification allowing for equifinality is described. Prediction within this methodology is a process of ensemble forecasting using a sample of parameter sets from the behavioural model space, with each sample weighted according to its likelihood measure to estimate prediction quantiles. This allows that different models may contribute to the ensemble prediction interval at different time steps and that the distributional form of the predictions may change over time. Any effects of model nonlinearity, covariation of parameter values and errors in model structure, input data or observed variables, with which the simulations are compared, are handled implicitly within this procedure. GLUE involves a number of choices that must be made explicit and can be therefore subjected to scrutiny and discussion. These include ways of combining information from different types of model evaluation or from different periods in a data assimilation context. An example application to rainfall-runoff modelling is used to illustrate the methodology, including the updating of likelihood measures.

AB - It may be endemic to mechanistic modelling of complex environmental systems that there are many different model structures and many different parameter sets within a chosen model structure that may be behavioural or acceptable in reproducing the observed behaviour of that system. This has been called the equifinality concept. The generalised likelihood uncertainty estimation (GLUE) methodology for model identification allowing for equifinality is described. Prediction within this methodology is a process of ensemble forecasting using a sample of parameter sets from the behavioural model space, with each sample weighted according to its likelihood measure to estimate prediction quantiles. This allows that different models may contribute to the ensemble prediction interval at different time steps and that the distributional form of the predictions may change over time. Any effects of model nonlinearity, covariation of parameter values and errors in model structure, input data or observed variables, with which the simulations are compared, are handled implicitly within this procedure. GLUE involves a number of choices that must be made explicit and can be therefore subjected to scrutiny and discussion. These include ways of combining information from different types of model evaluation or from different periods in a data assimilation context. An example application to rainfall-runoff modelling is used to illustrate the methodology, including the updating of likelihood measures.

KW - TOPMODEL

KW - Maimai catchment

KW - Rainfall-runoff modelling

KW - Parameter conditioning

KW - Prediction uncertainty

KW - GLUE

U2 - 10.1016/S0022-1694(01)00421-8

DO - 10.1016/S0022-1694(01)00421-8

M3 - Journal article

VL - 249

SP - 11

EP - 29

JO - Journal of Hydrology

JF - Journal of Hydrology

IS - 1-4

ER -