Toward a Model-Based Method for Gap Filling Latent and Sensible Heat Fluxes for a Semi-Arid Site

Persistent Link:
http://hdl.handle.net/10150/193333
Title:
Toward a Model-Based Method for Gap Filling Latent and Sensible Heat Fluxes for a Semi-Arid Site
Author:
Neal, Andrew
Issue Date:
2008
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:
The eddy covariance technique for measuring the exchange of mass and energy between the land surface and atmosphere yields data records with substantial gaps, reported to be as long as 30 to 40% of a time series annually (at a half-hourly time step). The application of these data sets in modeling studies as well as on varying time scales and under non-ideal conditions, requires some interpolation method to infer values for the missing data. This study will consider a neural network regression model for a flux record from a semi-arid riparian site and examine the model's responsiveness to variability in the data available for training. The neural network sensitivity to flux data used for training is evaluated. Model response worsened under reduced training data availability and was dependent on the characteristics of the data.
Type:
text; Electronic Thesis
Degree Name:
MS
Degree Level:
masters
Degree Program:
Hydrology; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Gupta, Hoshin V.
Committee Chair:
Hiller, Joseph G.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleToward a Model-Based Method for Gap Filling Latent and Sensible Heat Fluxes for a Semi-Arid Siteen_US
dc.creatorNeal, Andrewen_US
dc.contributor.authorNeal, Andrewen_US
dc.date.issued2008en_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.abstractThe eddy covariance technique for measuring the exchange of mass and energy between the land surface and atmosphere yields data records with substantial gaps, reported to be as long as 30 to 40% of a time series annually (at a half-hourly time step). The application of these data sets in modeling studies as well as on varying time scales and under non-ideal conditions, requires some interpolation method to infer values for the missing data. This study will consider a neural network regression model for a flux record from a semi-arid riparian site and examine the model's responsiveness to variability in the data available for training. The neural network sensitivity to flux data used for training is evaluated. Model response worsened under reduced training data availability and was dependent on the characteristics of the data.en_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineHydrologyen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGupta, Hoshin V.en_US
dc.contributor.chairHiller, Joseph G.en_US
dc.contributor.committeememberBrooks, Paulen_US
dc.contributor.committeememberShuttleworth W. Jamesen_US
dc.identifier.proquest2760en_US
dc.identifier.oclc659749793en_US
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