MERGING MEASUREMENT AND MODELING FOR MORE EFFICIENT HYDROLOGIC ANALYSIS

Persistent Link:
http://hdl.handle.net/10150/196068
Title:
MERGING MEASUREMENT AND MODELING FOR MORE EFFICIENT HYDROLOGIC ANALYSIS
Author:
Hinnell, Andrew Charles
Issue Date:
2009
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:
Models used as part of quantitative studies of vadose zone processes are becoming increasingly complex. However, even the most elaborate models can not capture the complex interactions between spatially distributed water, plant, and atmospheric components of the unsaturated flow system. These processes will always need to be approximated by relatively simple mathematical expressions with limited parameterization. Because of this, there is an ever increasing awareness among hydrologists of the need to describe and quantify these uncertainties to better understand the utility of model predictions and inform decisions concerning model development and data collection. Significant developments in the most recent generation of parameter estimation codes have facilitated the estimation of parameters and quantification of the associated uncertainty in the parameter estimates and model structure; however, these codes are computationally expensive. To facilitate the proposed analysis of more computationally efficient models are required.Computationally efficient models do not necessarily imply over simplified models In the appropriate context, simplifications are possible that reduce the complexity of the model but do not reduce the complexity of the system being represented by the model. I investigate a series of approaches to reduce the computational load of models, facilitating inverse analysis with readily available computing facilities.In light of the improvements to the methodology of parameter estimation, the success of the analysis still depends on the observed response to which the model is compared; the data and the information contained in the data. Given limited resources (both cost and technology) it is important to identify those data that will provide the greatest information about a system. To this end, the investigations presented here also investigate methods to identify informative data and to extract information from data effectively.
Type:
text; Electronic Dissertation
Keywords:
hydrology; measurements; models
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Hydrology; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Ferré, Paul A
Committee Chair:
Ferré, Ty

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleMERGING MEASUREMENT AND MODELING FOR MORE EFFICIENT HYDROLOGIC ANALYSISen_US
dc.creatorHinnell, Andrew Charlesen_US
dc.contributor.authorHinnell, Andrew Charlesen_US
dc.date.issued2009en_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.abstractModels used as part of quantitative studies of vadose zone processes are becoming increasingly complex. However, even the most elaborate models can not capture the complex interactions between spatially distributed water, plant, and atmospheric components of the unsaturated flow system. These processes will always need to be approximated by relatively simple mathematical expressions with limited parameterization. Because of this, there is an ever increasing awareness among hydrologists of the need to describe and quantify these uncertainties to better understand the utility of model predictions and inform decisions concerning model development and data collection. Significant developments in the most recent generation of parameter estimation codes have facilitated the estimation of parameters and quantification of the associated uncertainty in the parameter estimates and model structure; however, these codes are computationally expensive. To facilitate the proposed analysis of more computationally efficient models are required.Computationally efficient models do not necessarily imply over simplified models In the appropriate context, simplifications are possible that reduce the complexity of the model but do not reduce the complexity of the system being represented by the model. I investigate a series of approaches to reduce the computational load of models, facilitating inverse analysis with readily available computing facilities.In light of the improvements to the methodology of parameter estimation, the success of the analysis still depends on the observed response to which the model is compared; the data and the information contained in the data. Given limited resources (both cost and technology) it is important to identify those data that will provide the greatest information about a system. To this end, the investigations presented here also investigate methods to identify informative data and to extract information from data effectively.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjecthydrologyen_US
dc.subjectmeasurementsen_US
dc.subjectmodelsen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineHydrologyen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorFerré, Paul Aen_US
dc.contributor.chairFerré, Tyen_US
dc.contributor.committeememberFerré, Paul Aen_US
dc.contributor.committeememberGupta, Hoshin Vijaien_US
dc.contributor.committeememberYeh, Tian-Chyi J.en_US
dc.contributor.committeememberSchaap, Marcel G.en_US
dc.identifier.proquest10718en_US
dc.identifier.oclc659753513en_US
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