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Detection of structural inadequacy in process-based hydrological models : a particle-filtering approach.

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Detection of structural inadequacy in process-based hydrological models : a particle-filtering approach. / Smith, Paul; Beven, Keith J.; Tawn, Jonathan A.

In: Water Resources Research, Vol. 44, No. 1, 08.01.2008, p. W01410.

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@article{fc9b0489004e42c3997b891856116216,
title = "Detection of structural inadequacy in process-based hydrological models : a particle-filtering approach.",
abstract = "In recent years, increasing computational power has been used to weight competing hydrological models in a Bayesian framework to improve predictive power. This may suggest that for a given measure of association with the observed data, one hydrological model is superior to another. However, careful analyses of the residuals of the model fit are required to propose further improvements to the model. In this paper we consider an alternative method of analyzing the shortcomings in a hydrological model. The hydrological model parameters are treated as varying in time. Simulation using a particle filter algorithm then reveals the parameter distribution needed at each time to reproduce the observed data. The resulting parameter, and the corresponding model state, distributions can be analyzed to propose improvements to the hydrological model. A demonstrative example is presented using rainfall-runoff data from the Leaf River, United States. This indicates that even when explicitly representing the uncertainty of the observed rainfall and discharge series, the technique shows shortcomings in the model structure.",
author = "Paul Smith and Beven, {Keith J.} and Tawn, {Jonathan A.}",
year = "2008",
month = "1",
day = "8",
doi = "10.1029/2006WR005205",
language = "English",
volume = "44",
pages = "W01410",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "AMER GEOPHYSICAL UNION",
number = "1",

}

RIS

TY - JOUR

T1 - Detection of structural inadequacy in process-based hydrological models : a particle-filtering approach.

AU - Smith, Paul

AU - Beven, Keith J.

AU - Tawn, Jonathan A.

PY - 2008/1/8

Y1 - 2008/1/8

N2 - In recent years, increasing computational power has been used to weight competing hydrological models in a Bayesian framework to improve predictive power. This may suggest that for a given measure of association with the observed data, one hydrological model is superior to another. However, careful analyses of the residuals of the model fit are required to propose further improvements to the model. In this paper we consider an alternative method of analyzing the shortcomings in a hydrological model. The hydrological model parameters are treated as varying in time. Simulation using a particle filter algorithm then reveals the parameter distribution needed at each time to reproduce the observed data. The resulting parameter, and the corresponding model state, distributions can be analyzed to propose improvements to the hydrological model. A demonstrative example is presented using rainfall-runoff data from the Leaf River, United States. This indicates that even when explicitly representing the uncertainty of the observed rainfall and discharge series, the technique shows shortcomings in the model structure.

AB - In recent years, increasing computational power has been used to weight competing hydrological models in a Bayesian framework to improve predictive power. This may suggest that for a given measure of association with the observed data, one hydrological model is superior to another. However, careful analyses of the residuals of the model fit are required to propose further improvements to the model. In this paper we consider an alternative method of analyzing the shortcomings in a hydrological model. The hydrological model parameters are treated as varying in time. Simulation using a particle filter algorithm then reveals the parameter distribution needed at each time to reproduce the observed data. The resulting parameter, and the corresponding model state, distributions can be analyzed to propose improvements to the hydrological model. A demonstrative example is presented using rainfall-runoff data from the Leaf River, United States. This indicates that even when explicitly representing the uncertainty of the observed rainfall and discharge series, the technique shows shortcomings in the model structure.

U2 - 10.1029/2006WR005205

DO - 10.1029/2006WR005205

M3 - Journal article

VL - 44

SP - W01410

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 1

ER -