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Two-Source Energy Balance Model: Refinements and Lysimeter Tests in the Southern High Plains

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 55(2): 551-562. (doi: 10.13031/2013.41385) @2012
Authors:   P. D. Colaizzi, S. R. Evett, T. A. Howell, P. H. Gowda, S. A. O’Shaughnessy, J. A. Tolk, W. P. Kustas, M. C. Anderson
Keywords:   Clumping index, Evapotranspiration, Fractional cover, Latent heat flux, Radiometric temperature, Remote sensing, Row crops, Texas

A thermal two-source energy balance model (TSEB-N95) was evaluated for calculating daily evapotranspiration (ET) of corn, cotton, grain sorghum, and wheat in a semiarid, advective environment. Crop ET was measured with large, monolithic weighing lysimeters. The TSEB-N95 model solved the energy budget of soil and vegetation using a series resistance network, and one-time-of-day latent heat flux calculations were scaled to daily ET using the ASCE Standardized Reference ET equation for a short crop. The TSEB-N95 model included several refinements, including a geometric method to account for the nonrandom spatial distribution of vegetation for row crops with partial canopy cover, where crop rows were modeled as elliptical hedgerows. This geometric approach was compared to the more commonly used, semi-empirical clumping index approach. Both approaches resulted in similar ET calculations, but the elliptical hedgerow approach performed slightly better. Using the clumping index, root mean squared error, mean absolute error, and mean bias error were 1.0 (22%), 0.79 (17%), and 0.093 (2.0%) mm d-1, respectively, between measured and calculated daily ET for all crops, where percentages were of the measured mean ET (4.62 mm d-1). Using the elliptical hedgerow, root mean squared error, mean absolute error, and mean bias error were 0.86 (19%), 0.69 (15%), and 0.17 (3.6%) mm d-1, respectively, between measured and calculated daily ET for all crops. The refinements to TSEB-N95 will improve the accuracy of remote sensing-based ET maps, which is imperative for water resource management.

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