Footprint prediction of scalar fluxes using a Markovian analysis

Abstract

The contribution of upwind sources to measurements of vertical scalar flux density as a function of fetch ('footprint') is predicted using a Markovian simulation of fluid particle trajectories. Results suggest that both footprint peak position and magnitude change dramatically with surface roughness, thermal stability and observation levels. Results also indicate that the much used 100 to 1 fetch-to-height ratio grossly underestimates fetch requirements when observations are made above smooth surfaces, in stable conditions or at high observation levels.

Actual Evapotranspiration (Water Use) Assessment of the Colorado River Basin at the Landsat Resolution Using the Operational Simplified Surface Energy Balance Model

“…However, the point distribution in Figure 6 shows that the SSEBop model captured the ET variability well even without footprint analysis. In general, the footprint of a point of observation is affected by thermal stability, surface roughness, observation levels, and wind speed and direction [51]. Since the wind direction is variable throughout the year, an exact footprint analysis is simply impossible to carryout [52].…”

Separating physiologically and directionally induced changes in PRI using BRDF models

“…The upward pointing probe is equipped with a cosine receptor (PP-Systems) to correct sky irradiance measurements for varying solar altitudes. The downward looking probe measures canopy reflectance at a zenith angle of 62° to account for canopy clumping (Chen and Black, 1991) The sampling area for the eddy covariance measurements or "flux-footprint" depends on windspeed and atmospheric conditions (Leclerc and Thurtell, 1990), and can range from a few hectares to a few square-kilometres (Schmid and Lloyd, 1999). However, the area to which flux measurements are most sensitive, also referred to as peak footprint, is much smaller and covers a radius of only 100-300 m (Blanken et al, 2001, Kljun et al, 2004.…”

Assessing eddy-covariance flux tower location bias across the Fluxnet-Canada Research Network based on remote sensing and footprint modelling

“…Although many advances have been made in footprint modelling and experimental validation (e.g. Leclerc and Thurtell, 1990;Wilson and Swaters, 1991;Horst and Weil, 1994;Finn et al, 1996;Kormann and Meixner, 2001;Leclerc et al, 2003), the influence of the variability of footprint and vegetation heterogeneities on EC flux measurements has not yet been investigated fully.…”