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Publications [#281490] of Gabriel G. Katul

Papers Published

  1. Katul, G; Oren, R; Ellsworth, D; Hsieh, CI; Phillips, N; Lewin, K, Lagrangian dispersion model for predicting CO2 sources, sinks, and fluxes in a uniform loblolly pine (Pinus taeda L.) stand, Journal of Geophysical Research, vol. 102 no. 8 (April, 1997), pp. 9309-9321, ISSN 0148-0227 [doi]
    (last updated on 2023/06/01)

    Abstract:
    A canopy Lagrangian turbulent scalar transport model for predicting scalar fluxes, sources, and sinks within a forested canopy was tested using CO2 concentration and flux measurements. The model formulation is based on the localized near-field theory (LNF) proposed by Raupach [1989a, b]. Using the measured mean CO2 concentration profile, the vertical velocity variance profile, and the Lagrangian integral timescale profile within and above a forested canopy, the proposed model predicted the CO2 flux and source (or sink) profiles. The model testing was carried out using eddy correlation measurements at 9 m in a uniform 13 m tall Pinus taeda L. (loblolly pine) stand at the Blackwood division of the Duke Forest near Durham, North Carolina. The tree height and spacing are relatively uniform throughout. The measured vertical profile leaf area index (LAI) was characterized by three peaks, with a maximum LAI occurring at 6.5 m, in qualitative agreement with the LNF source-sink predicted profile. The LNF CO2 flux predictions were in better agreement with eddy correlation measurements (coefficient of determination r2 = 0.58; and standard error of estimate equal to 0.16 mg kg-1 m s-1) than K theory. The model reproduced the mean diurnal CO2 flux, suggesting better performance over longer averaging time periods. Two key simplifications to the LNF formulation were considered, namely, the near-Gaussian approximation to the vertical velocity and the absence of longitudinal advection. It was found that both of these assumptions were violated throughout the day, but the resulting CO2 flux error at 9 m was not strongly related to these approximations. In contrast to the forward LNF approach utilized by other studies, this investigation demonstrated that the inverse LNF approach is sensitive to near-field corrections.