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ARS Home » Pacific West Area » Tucson, Arizona » SWRC » Research » Publications at this Location » Publication #227978
Title: Toward Model Based Gap Filling Methods for Water and Energy Fluxes in a Semi-Arid System 1962

Author
item NEAL, A. - UNIVERSITY OF ARIZONA
item GUPTA, H. - UNIVERSITY OF ARIZONA
item Scott, Russell - Russ

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 3/10/2008
Publication Date: 5/15/2008
Citation: Neal, A., Gupta, H., Scott, R.L. 2008. Toward Model Based Gap Filling Methods for Water and Energy Fluxes in a Semi-Arid System. Eos Trans. AGU, 89(23), Jt. Assem. Suppl., Abstract H41A-02.

Interpretive Summary:

Technical Abstract: Continuous records of water and energy fluxes are critical to understand the dynamics of land-atmosphere interactions across various time scales. While measurement systems have become more capable at capturing the processes of energy and moisture exchange, the loss of data reduces the ability to draw conclusions about land-atmosphere interactions and complicates the problem of model calibration. This study employs two model-based methods to interpolate missing data points for flux measurements taken at a mesquite woodland site in southern Arizona. One model, a simple, deterministic land-atmosphere scheme appears to have shortcomings as to the ability to depict the flux behavior at the site. A second model, using neural network techniques, produces satisfactory results for gap filling. The analysis performed with the neural network suggests that the fluxes on the site follow a non-normal (gamma) distribution and were thus normalized before being applied to the model. Using model results as gap filled flux records has been recommended based on studies in humid regions, and is further supported here for semi-arid regions. Obtaining continuous records of fluxes enables more complete analysis of the mass and energy balance for a site, both for modeling studies and ecological and hydrological intercomparison. Such work will help us better understand how to refine methods for gap filling data, especially for semi-arid regions.