A Lagrangian dispersion model for predicting CO2 sources, sinks, and fluxes in a uniform loblolly pine (Pinus taeda L.) stand

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.

[1]  K. G. McNaughton,et al.  A ‘Lagrangian’ revision of the resistors in the two-layer model for calculating the energy budget of a plant canopy , 1995 .

[2]  B. Hurk,et al.  Implementation of near-field dispersion in a simple two-layer surface resistance model , 1995 .

[3]  G. Katul A model for sensible heat flux probability density function for near-neutral and slightly-stable atmospheric flows , 1994 .

[4]  E. Yee,et al.  On the moments approximation method for constructing a Lagrangian Stochastic model , 1994 .

[5]  D. Lenschow,et al.  How long is long enough when measuring fluxes and other turbulence statistics , 1994 .

[6]  J. Finnigan,et al.  Atmospheric Boundary Layer Flows: Their Structure and Measurement , 1994 .

[7]  T. Flesch,et al.  Flow Boundaries in Random-Flight Dispersion Models: Enforcing the Well-Mixed Condition , 1993 .

[8]  D. Paslier,et al.  Net Exchange of CO2 in a Mid-Latitude Forest , 1993, Science.

[9]  D. Baldocchi A lagrangian random-walk model for simulating water vapor, CO2 and sensible heat flux densities and scalar profiles over and within a soybean canopy , 1992 .

[10]  T. W. Horst,et al.  Footprint estimation for scalar flux measurements in the atmospheric surface layer , 1992 .

[11]  A. Dolman,et al.  Lagrangian and K-theory approaches in modelling evaporation from sparse canopies , 1991 .

[12]  W. Mccomb,et al.  The physics of fluid turbulence. , 1990 .

[13]  Monique Y. Leclerc,et al.  Footprint prediction of scalar fluxes using a Markovian analysis , 1990 .

[14]  M. Raupach Applying Lagrangian fluid mechanics to infer scalar source distributions from concentration profiles in plant canopies , 1989 .

[15]  Michael R. Raupach,et al.  A practical Lagrangian method for relating scalar concentrations to source distributions in vegetation canopies , 1989 .

[16]  T. Meyers,et al.  A comparison of models for deriving dry deposition fluxes of O3 and SO2 to a forest canopy , 1988 .

[17]  G. W. Thurtell,et al.  Measurements and Langevin simulations of mean tracer concentration fields downwind from a circular line source inside an alfalfa canopy , 1988 .

[18]  Tilden P. Meyers,et al.  Modelling the plant canopy micrometeorology with higher-order closure principles , 1987 .

[19]  D. Thomson Criteria for the selection of stochastic models of particle trajectories in turbulent flows , 1987, Journal of Fluid Mechanics.

[20]  Brian L. Sawford,et al.  Generalized random forcing in random‐walk turbulent dispersion models , 1986 .

[21]  T. Meyers,et al.  Testing of a higher-order closure model for modeling airflow within and above plant canopies , 1986 .

[22]  Donald H. Lenschow,et al.  Length Scales in the Convective Boundary Layer , 1986 .

[23]  B. L. Sawford,et al.  Lagrangian Statistical Simulation of Concentration Mean and Fluctuation Fields. , 1985 .

[24]  M. Raupach Near-field dispersion from instantaneous sources in the surface layer , 1983 .

[25]  D. Thomson,et al.  A random walk model of dispersion in the diabatic surface layer , 1983 .

[26]  D. Thomson,et al.  Calculation of particle trajectories in the presence of a gradient in turbulent-velocity variance , 1983 .

[27]  M. Raupach,et al.  Markov-chain simulation of particle dispersion in inhomogeneous flows: The mean drift velocity induced by a gradient in Eulerian velocity variance , 1982 .

[28]  G. W. Thurtell,et al.  Numerical simulation of particle trajectories in inhomogeneous turbulence, I: Systems with constant turbulent velocity scale , 1981 .

[29]  H. Fischer Mixing in Inland and Coastal Waters , 1979 .

[30]  J. D. Reid,et al.  Markov Chain Simulations of Vertical Dispersion in the Neutral Surface Layer for Surface and Elevated Releases , 1979 .

[31]  J. Deardorff Closure of second‐ and third‐moment rate equations for diffusion in homogeneous turbulence , 1978 .

[32]  G. Csanady Turbulent Diffusion in the Environment , 1973 .

[33]  J. Lumley,et al.  Some measurements of particle velocity autocorrelation functions in a turbulent flow , 1971, Journal of Fluid Mechanics.

[34]  B. Hicks,et al.  Flux‐gradient relationships in the constant flux layer , 1970 .

[35]  S. Corrsin,et al.  Estimates of the Relations between Eulerian and Lagrangian Scales in Large Reynolds Number Turbulence , 1963 .

[36]  G. Batchelor,et al.  The theory of homogeneous turbulence , 1954 .

[37]  M. Parlange,et al.  Local isotropy and anisotropy in the sheared and heated atmospheric surface layer , 1995 .

[38]  B. Sawford Recent developments in the Lagrangian stochastic theory of turbulent dispersion , 1993 .

[39]  Raupach,et al.  Challenges in Linking Atmospheric CO2 Concentrations to Fluxes at Local and Regional Scales , 1992 .

[40]  Rex Britter,et al.  A random walk model for dispersion in inhomogeneous turbulence in a convective boundary layer , 1989 .

[41]  G. W. Thurtell Comments on using K-theory within and above the plant canopy to model diffusion processes , 1989 .

[42]  M. Raupach Canopy Transport Processes , 1988 .

[43]  I. R. Cowan,et al.  Transfer processes in plant canopies in relation to stomatal characteristics. , 1987 .

[44]  E. F. Bradley,et al.  Flux-Gradient Relationships in a Forest Canopy , 1985 .

[45]  J. C. R. Hunt,et al.  Diffusion in the Stable Boundary Layer , 1984 .

[46]  C. Gardiner Handbook of Stochastic Methods , 1983 .

[47]  S. Corrsin Limitations of Gradient Transport Models in Random Walks and in Turbulence , 1975 .

[48]  J. Angell Lagrangian-Eulerian Time-Scale Relationship Estimated from Constant Volume Balloon Flights Past a Tall Tower , 1975 .

[49]  J. Lumley,et al.  A First Course in Turbulence , 1972 .

[50]  J. Angell,et al.  Lagrangian‐Eulerian time‐scale ratios estimated from constant volume balloon flights past a tall tower , 1971 .

[51]  J. R. Philip Diffusion by Continuous Movements , 1968 .

[52]  John L. Lumley,et al.  The structure of atmospheric turbulence , 1964 .

[53]  S. Corrsin,et al.  Progress Report on Some Turbulent Diffusion Research , 1959 .