Latent and sensible heat flux predictions from a uniform pine forest using surface renewal and flux variance methods

Abstract

A surface renewal model that links organized eddy motion to the latent and sensible heat fluxes is tested with eddy correlation measurements carried out in a 13 m tall uniform Loblolly pine plantation in Duke Forest, Durham, North Carolina. The surface renewal model is based on the occurance of ramp-like patterns in the scalar concentration measurements. To extract such ramp-like patterns from Eulerian scalar concentration measurements, a newly proposed time-frequency filtering scheme is developed and tested. The time-domain filtering is carried out using compactly-supported orthononnal wavelets in conjunction with the "Universal Wavelet Thresholding" approach of Donoho and Johnstone, while the frequency filtering is carried out by a band-pass sine filter centered around the ramp-occurrence frequency as proposed by other studies. The method was separately tested for heat and water vapour with good agreement between eddy correlation flux measurements and model predictions. The usefulness of the flux-variance method to predict sensible and latent heat fluxes is also considered. Our measurements suggest that the simple flux-variance method reproduces the measured heat and momentum fluxes despite the fact that the variances were measured within the roughness sublayer and not in the surface layer. Central to the predictions of water vapour fluxes using the flux-variance approach is the similarity between heat and water vapour transport by the turbulent air flow. This assumption is also investigated for this uniform forest terrain.

A comparison of new and existing equations for estimating sensible heat flux using surface renewal and similarity concepts

“…[51] Although mainly for equation (24), the flux-variance method has also been tested under nonideal field conditions [Weaver, 1990;De Bruin et al, 1991;Katul et al, 1995Katul et al, , 1996Wesson et al, 2001]. Equations (22) and (24) were analyzed for the experiments over wheat and grapevines by Castellví [2004] and for the olive orchard by Castellví and Martínez-Cob [2005].…”

Computational efficiency for the surface renewal method

“…Ramps are an identifiable feature in the measured temperature trace above any natural surface, yet determining the characteristic ramp geometry from high-frequency data requires an efficient, robust, and preferably automated procedure. There are several methods to determine ramp geometry, including visual detection , lowpass filtering (Katul et al, 1996;Paw U et al, 1995), wavelet analysis (Gao and Li, 1993), and structure functions (Spano et al, 1997). Structure functions in particular provide both objective criteria to detect ramps and an efficient method to tabulate statistics of time series data, and use of structure functions has become the predominant method used for SR.…”

“…Because fewer, lower-cost sensors are required, the SR method theoretically can be used for general applied monitoring (Paw U et al, 2005;Spano et al, 2000). Another advantage of SR is the ability to measure flux very near the surface or near the top of the plant canopy (Katul et al, 1996;Paw U et al, 1992). By taking measurements very close to the surface, the measurement fetch is reduced and the effective "flux footprint" is smaller (Castellví, 2012), yielding a more localized flux estimate.…”

Computational Efficiency for the Surface Renewal Method

“…Ramps are an identifiable feature in the measured temperature trace above any natural surface, yet determining the characteristic ramp geometry from high frequency data requires an efficient, robust, and preferably automated procedure. There are several methods to determine ramp geometry, including visual detection (Shaw and 130 Gao, 1989), low pass filtering (Katul et al, 1996;Paw U et al, 1995), wavelet analysis (Gao and Li, 1993), and structure functions (Spano et al, 1997). Structure functions in particular provide both objective criteria to detect ramps and an efficient method to tabulate statistics of time series data.…”

“…By requiring fewer sensors, cost is reduced, which expands direct flux measurement from research applications to more general 20 applied monitoring (Paw U et al, 2005a;Spano et al, 2000). Another advantage of SR over methods such as EC is that SR can measure flux very near the surface or near the top of the plant canopy (Katul et al, 1996;Paw U et al, 1992). By taking measurements very close to the surface, the measurement fetch is reduced and the "flux footprint" is smaller (Castellví, 2012), yielding a more localized flux estimate.…”