Applying a Lagrangian dispersion analysis to infer carbon dioxide and latent heat fluxes in a corn canopy

Abstract

Warland and Thurtell (2000) proposed an analytical dispersion Lagrangian analysis (hereafter WT analysis) to relate the mean scalar concentration field to source profiles inside the canopy. The first objective of this study was to evaluate the performance of the WT analysis with existing turbulence statistics parameterizations in a corn canopy, by comparing its inferred net ecosystem CO 2 exchange (NEE) and latent heat flux ( λE) with eddy covariance measurements. The second objective was to assess the performance of the WT analysis to infer the soil CO 2 flux. Four parameterizations of turbulence statistics were used to estimate Lagrangian time scale ( T L ) and standard deviation of vertical wind velocity ( σ w ) profiles. The estimated T L and σ w profiles were then corrected for atmospheric stability conditions. The field experiment was carried out in a corn field from August to October 2007 and 2008. Profiles of water vapour and CO 2 mixing ratios were measured using a multiport sampling system connected to an infrared gas analyzer. Wind velocity within and above the canopy and eddy covariance measurements over the canopy were taken. The soil respiration, estimated using the WT analysis, was compared to estimates obtained by an empirical model. WT analysis fluxes showed good correlation ( R 2 = 0.77–0.88) with NEE and λE obtained by the eddy covariance technique, but overestimated net fluxes, especially when corrections for atmospheric stability were applied. The optimization of T L and σ w profiles using in-canopy turbulence measurements improved the agreement between measured and modeled NEE and λE. Inferred soil CO 2 fluxes were underestimated and were poorly correlated ( R 2 = 0.02–0.01) with estimates obtained using an empirical model based on soil temperature. This poor performance in estimating the soil respiration is likely caused by the decoupling between inside and above canopy flows.