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United States Department of Agriculture

Agricultural Research Service

Title: AN EVALUATION OF TWO MODELS FOR ESTIMATION OF THE ROUGHNESS HEIGHT FOR HEAT TRANSFER BETWEEN THE LAND SURFACE AND THE ATMOSPHERE

Authors
item Su, Z - WAGENINGEN UNIV, NETHLNDS
item Schmugge, Thomas
item Kustas, William
item Massman, W - USDA/FS, FT COLLINS, CO

Submitted to: Journal of Applied Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 17, 2001
Publication Date: November 15, 2001

Interpretive Summary: The heat transfer coefficient for the exchange of energy between the land surface and atmosphere depends on the roughness structure of the surface, specifically in this case the geometry of the plants. This paper presents the results of two models used to calculate these transfer coefficients from the plant geometry and compares the results with field estimates of the coefficients. The results are encouraging for the general use of these models.

Technical Abstract: Roughness height for heat transfer is a crucial parameter in estimation of heat transfer between the land surface and the atmosphere. Although many empirical formulations have been proposed over the past few decades, the uncertainties associated with these formulations are shown to be large, especially, over sparse canopies. In this contribution, we evaluate two recently proposed models of which one is based on the 'localized near-field' Lagrangian theory, and the other is derived by fitting simulation results of a simple bulk transfer model. Both models are reported performing superior to previous empirical formulations; both also require significantly more information which are related to the canopy and to the flow characteristics. Three experimental data sets that have sufficient information on both canopy and the flow are used to evaluate the performance of the two models. The results of the model performances are judged against the experimentally determined fluxes. Conclusions are reached regarding both the model performances and the uncertainties related to the experimental data.

Last Modified: 9/1/2014
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