Parameter estimation for hydrometeorological models using multi-criteria methods

Persistent Link:
http://hdl.handle.net/10150/282748
Title:
Parameter estimation for hydrometeorological models using multi-criteria methods
Author:
Bastidas, Luis Alberto, 1950-
Issue Date:
1998
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
There are three components of error in the ability of land-atmosphere models (e.g., BATS, SiB, etc.) to simulate/predict observed land-surface state variables and output fluxes (e.g. lambdaE, H, Tg, Q, etc.). The first is caused by model structural error associated with simplifications and/or inadequacies in the functional representations of underlying physical processes. The second component is measurement error associated with the input and output data. The third is caused by error in specification of the values of the model parameters. Automatic parameter tuning (model calibration) methods allow minimizing of the parameter error, thereby obtaining an estimate of the remaining error components. This work describes an automatic multi-criteria approach and its use to tune all 27 parameters of the BATS model using data measured in the field. The parameters were adjusted to simultaneously optimize the ability of the model to reproduce observed values of several output fluxes and/or state variables (e.g., latent heat flux, sensible heat flux, ground temperature, etc.). The results indicate that not only does the procedure result in conceptually reasonable and consistent parameter estimates, but the calibrated model is able to provide significant improvement in performance (33% or more reduction in error) over the "un-calibrated" model (i.e., using the BATS default parameter values for the associated region). Substantial improvements of this kind can have important implications for studies that seek to evaluate alternative model structures or to regionalize parameters. To reduce the dimensionality of the optimization problem a multi-criteria extension of the Regionalized Sensitivity Analysis (RSA) has been developed.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Hydrology.; Statistics.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Hydrology and Water Resources
Degree Grantor:
University of Arizona
Advisor:
Sorooshian, Soroosh

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleParameter estimation for hydrometeorological models using multi-criteria methodsen_US
dc.creatorBastidas, Luis Alberto, 1950-en_US
dc.contributor.authorBastidas, Luis Alberto, 1950-en_US
dc.date.issued1998en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThere are three components of error in the ability of land-atmosphere models (e.g., BATS, SiB, etc.) to simulate/predict observed land-surface state variables and output fluxes (e.g. lambdaE, H, Tg, Q, etc.). The first is caused by model structural error associated with simplifications and/or inadequacies in the functional representations of underlying physical processes. The second component is measurement error associated with the input and output data. The third is caused by error in specification of the values of the model parameters. Automatic parameter tuning (model calibration) methods allow minimizing of the parameter error, thereby obtaining an estimate of the remaining error components. This work describes an automatic multi-criteria approach and its use to tune all 27 parameters of the BATS model using data measured in the field. The parameters were adjusted to simultaneously optimize the ability of the model to reproduce observed values of several output fluxes and/or state variables (e.g., latent heat flux, sensible heat flux, ground temperature, etc.). The results indicate that not only does the procedure result in conceptually reasonable and consistent parameter estimates, but the calibrated model is able to provide significant improvement in performance (33% or more reduction in error) over the "un-calibrated" model (i.e., using the BATS default parameter values for the associated region). Substantial improvements of this kind can have important implications for studies that seek to evaluate alternative model structures or to regionalize parameters. To reduce the dimensionality of the optimization problem a multi-criteria extension of the Regionalized Sensitivity Analysis (RSA) has been developed.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectHydrology.en_US
dc.subjectStatistics.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSorooshian, Sorooshen_US
dc.identifier.proquest9906516en_US
dc.identifier.bibrecord.b3886681xen_US
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