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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.orgCitation: 5th National Decennial Irrigation Conference Proceedings, 5-8 December 2010, Phoenix Convention Center, Phoenix, Arizona USA IRR10-9701.
Authors: Paul D Colaizzi, William P Kustas, Martha C Anderson, Steven R Evett, Terry A Howell, Prasanna H Gowda, Susan A O’Shaughnessy, Judy A Tolk
Keywords: Evapotranspiration, remote sensing, row crops, Texas, latent heat flux, clumping index, fractional cover, radiometric temperature
A thermal two-source energy balance model (TSM) was evaluated for predicting 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 TSM solved the energy budget of soil and vegetation using a series resistance network, and one-time-of-day latent heat flux estimates were scaled to daily ET using the ASCE Standardized Reference ET equation for a short crop. The TSM 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 estimates, 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 modeled daily ET for all crops, where percentages were of the observed 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 modeled daily ET for all crops. The TSM refinements will improve the accuracy of remote sensing-based ET maps, which is imperative for water resource management.