Toward a Model-Based Method for Gap Filling Latent and Sensible Heat Fluxes for a Semi-Arid Site
AdvisorGupta, Hoshin V.
Committee ChairHiller, Joseph G.
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PublisherThe University of Arizona.
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.
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.