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A comparison of non-linear least square and GLUE for model calibration and uncertainty estimation for pesticide transport in soils

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A comparison of non-linear least square and GLUE for model calibration and uncertainty estimation for pesticide transport in soils. / Zhang, Danrong; Beven, Keith; Mermoud, André.
In: Advances in Water Resources, Vol. 29, No. 12, 12.2006, p. 1924-1933.

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Zhang D, Beven K, Mermoud A. A comparison of non-linear least square and GLUE for model calibration and uncertainty estimation for pesticide transport in soils. Advances in Water Resources. 2006 Dec;29(12):1924-1933. doi: 10.1016/j.advwatres.2006.02.004

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Zhang, Danrong ; Beven, Keith ; Mermoud, André. / A comparison of non-linear least square and GLUE for model calibration and uncertainty estimation for pesticide transport in soils. In: Advances in Water Resources. 2006 ; Vol. 29, No. 12. pp. 1924-1933.

Bibtex

@article{a247953188be42528344f8d965fcc09b,
title = "A comparison of non-linear least square and GLUE for model calibration and uncertainty estimation for pesticide transport in soils",
abstract = "The problems of calibrating soil hydraulic and transport parameters are well documented, particularly when data are limited. Programs such as CXTFIT, UUCODE and PEST, based on well established principles of statistical inference, will often provide good fits to limited observations giving the impression that a useful model of a particular soil system has been obtained. This may be the case, but such an approach may grossly underestimate the uncertainties associated with future predictions of the system and resulting dependent variables. In this paper, this is illustrated by an application of CXTFIT within the generalised likelihood uncertainty estimation (GLUE) approach to model calibration which is based on a quite different philosophy. CXTFIT gives very good fits to the observed breakthrough curves for several different model formulations, resulting in very small parameter uncertainty estimates. The application of GLUE, however, shows that much wider ranges of parameter values can provide acceptable fits to the data. The wider range of potential outcomes should be more robust in model prediction, especially when used to constrain field scale models.",
keywords = "CXTFIT, GLUE, Atrazine, Bromide, Tracer, Dispersion, Column scale, Solute transport",
author = "Danrong Zhang and Keith Beven and Andr{\'e} Mermoud",
year = "2006",
month = dec,
doi = "10.1016/j.advwatres.2006.02.004",
language = "English",
volume = "29",
pages = "1924--1933",
journal = "Advances in Water Resources",
publisher = "Elsevier Limited",
number = "12",

}

RIS

TY - JOUR

T1 - A comparison of non-linear least square and GLUE for model calibration and uncertainty estimation for pesticide transport in soils

AU - Zhang, Danrong

AU - Beven, Keith

AU - Mermoud, André

PY - 2006/12

Y1 - 2006/12

N2 - The problems of calibrating soil hydraulic and transport parameters are well documented, particularly when data are limited. Programs such as CXTFIT, UUCODE and PEST, based on well established principles of statistical inference, will often provide good fits to limited observations giving the impression that a useful model of a particular soil system has been obtained. This may be the case, but such an approach may grossly underestimate the uncertainties associated with future predictions of the system and resulting dependent variables. In this paper, this is illustrated by an application of CXTFIT within the generalised likelihood uncertainty estimation (GLUE) approach to model calibration which is based on a quite different philosophy. CXTFIT gives very good fits to the observed breakthrough curves for several different model formulations, resulting in very small parameter uncertainty estimates. The application of GLUE, however, shows that much wider ranges of parameter values can provide acceptable fits to the data. The wider range of potential outcomes should be more robust in model prediction, especially when used to constrain field scale models.

AB - The problems of calibrating soil hydraulic and transport parameters are well documented, particularly when data are limited. Programs such as CXTFIT, UUCODE and PEST, based on well established principles of statistical inference, will often provide good fits to limited observations giving the impression that a useful model of a particular soil system has been obtained. This may be the case, but such an approach may grossly underestimate the uncertainties associated with future predictions of the system and resulting dependent variables. In this paper, this is illustrated by an application of CXTFIT within the generalised likelihood uncertainty estimation (GLUE) approach to model calibration which is based on a quite different philosophy. CXTFIT gives very good fits to the observed breakthrough curves for several different model formulations, resulting in very small parameter uncertainty estimates. The application of GLUE, however, shows that much wider ranges of parameter values can provide acceptable fits to the data. The wider range of potential outcomes should be more robust in model prediction, especially when used to constrain field scale models.

KW - CXTFIT

KW - GLUE

KW - Atrazine

KW - Bromide

KW - Tracer

KW - Dispersion

KW - Column scale

KW - Solute transport

U2 - 10.1016/j.advwatres.2006.02.004

DO - 10.1016/j.advwatres.2006.02.004

M3 - Journal article

VL - 29

SP - 1924

EP - 1933

JO - Advances in Water Resources

JF - Advances in Water Resources

IS - 12

ER -