Modelling night-time ecosystem respiration by a constrained source optimization method

One of the main challenges to quantifying ecosystem carbon budgets is properly quantifying the magnitude of night-time ecosystem respiration. Inverse Lagrangian dispersion analysis provides a promising approach to addressing such a problem when measured mean CO2 concentration profiles and nocturnal velocity statistics are available. An inverse method, termed 'Constrained Source Optimization' or CSO, which couples a localized near-field theory (LNF) of turbulent dispersion to respiratory sources, is developed to estimate seasonal and annual components of ecosystem respiration. A key advantage to the proposed method is that the effects of variable leaf area density on flow statistics are explicitly resolved via higher-order closure principles. In CSO, the source distribution was computed after optimizing key physiological parameters to recover the measured mean concentration profile in a least-square fashion. The proposed method was field-tested using 1 year of 30-min mean CO2 concentration and CO2 flux measurements collected within a 17-year-old (in 1999) even-aged loblolly pine (Pinus taeda L.) stand in central North Carolina. Eddy-covariance flux measurements conditioned on large friction velocity, leaf-level porometry and forest-floor respiration chamber measurements were used to assess the performance of the CSO model. The CSO approach produced reasonable estimates of ecosystem respiration, which permits estimation of ecosystem gross primary production when combined with daytime net ecosystem exchange (NEE) measurements. We employed the CSO approach in modelling annual respiration of above-ground plant components (c. 214 g C m-2 year-1) and forest floor (c. 989 g C m-2 year-1) for estimating gross primary production (c. 1800 g C m-2 year-1) with a NEE of c. 605 g C m-2 year-1 for this pine forest ecosystem. We conclude that the CSO approach can utilise routine CO2 concentration profile measurements to corroborate forest carbon balance estimates from eddy-covariance NEE and chamber-based component flux measurements.

Duke Scholars

Author Gabriel G. Katul Civil and Environmental Engineering