Agricultural activity and, therefore, the production or uptake of trace ases by crops, is variable at a very small scale over typical European agricultural sites. Thus a quantitative description of the surface flux budgets of atmospheric trace gases on a regional scale over highly inhomogeneous areas is not trivial although fertilizer volatilization from agricultural fields plays an important role in greenhouse warming (e.g., N2O).
In the present study, a 3-dimensional Stochastic Particle Dispersion Model (SPDM), satisfying the well-mixed condition, is adapted to allow for backward trajectories in order to determine the upwind surface area of influence for an atmospheric flux or concentration measurement. The size of this source area is governed by the height of the measurement, the surface roughness, atmospheric stability and the lateral components of turbulence.
For that purpose, calculating trajectories backwards in time has many advantages, as backward trajectories are released at a point, which stands for the sensor location and 'flow back' to the surface area source. The footprint method (Flesch et al. 1995) can be used to determine the source area for a measured trace gas flux at a given height. For inhomogeneous surfaces, a touchdown catalogue may then help to estimate the emission rates of individual plots. In the present simulations, special attention is paid to the characteristics of roughness sublayer turbulence, which are easy to implement in a SPDM.
The approach is tested using a data set from a field experiment on a flat agricultural site in Switzerland with small-scale variations in vegetative cover and thus source / uptake conditions. For trace gas fluxes measured at 15 m above ground, the footprints are determined and their temporal variability is shown to be explainable - at least partly - through the varying importance of contributions from individual plots, above which the 'surface' exchange was simultaneously measured. It is furthermore shown, that the results are sensitive to the description (parametrization) of turbulence statistics within the roughness sublayer as employed in the backward trajectory simulations.
Reference: Flesch, T. K., Wilson, J. D., and Yee E. 1995: 'Backward-Time Lagrangian Stochastic Dispersion Models and Their Application to Estimate Gaseous Emissions',J. Appl. Meteorol., 34, 1320-1332.
Symposium on Interdisciplinary Issues in Atmospheric Chemistry