Filling gaps in evapotranspiration measurements for water budget studies: Evaluation of a Kalman filtering approach

dc.contributor.affiliationSchool of Environmental Sciences
dc.contributor.authorAlavi, Nasim
dc.contributor.authorWarland, Jon
dc.contributor.authorBerg, Aaron A.
dc.date.accessioned2009-11-17T16:31:21Z
dc.date.available2009-11-17T16:31:21Z
dc.date.issuedDec-06
dc.degree.departmentSchool of Environmental Sciencesen
dc.description.abstractMissing data in long-term eddy covariance measurements of latent heat flux produce errors in the estimation of evapotranspiration and the water budget. Because no standard method of gap filling has been widely accepted, identification of optimal filling methods for gaps is crucial for determining total evapotranspiration. In this study we evaluate the application of a Kalman filter for filling gaps in latent heat flux data collected from an agricultural research station. The filtering approach was compared with several gap-filling methods including mean diurnal variation, multiple regressions, 2-week average Priestley–Taylor coefficient, and multiple imputation. The results demonstrated that a Kalman filtering approach developed using the relationship between latent heat flux, available energy, and vapour pressure deficit provides a closer approximation of the original data and introduces smaller errors than the other methods evaluated. Evaluation of the Kalman filter approach demonstrates the efficiency of this technique in replacing data in both small and large gaps of up to several days.en_US
dc.identifier.citationAlavi, N., Warland, J. S., and Berg, A. A. (2006) "Filling gaps in evapotranspiration measurements for water budget studies: Evaluation of a Kalman filtering approach." Agricultural and Forest Meteorology 141.1 (2006): 57-66. https://doi.org/10.1016/j.agrformet.2006.09.011
dc.identifier.urihttp://hdl.handle.net/10214/2088
dc.language.isoenen
dc.publisherElsevieren_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjecteddy covarianceen_US
dc.subjectmissing dataen_US
dc.subjectmultiple imputationen_US
dc.subjectrecursive estimationen_US
dc.subjectdata fillingen_US
dc.titleFilling gaps in evapotranspiration measurements for water budget studies: Evaluation of a Kalman filtering approachen_US
dc.typeArticleen

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