Footprint methods to separate N2O emission rates from adjacent paddock areas
Using micrometeorological techniques to measure greenhouse gas emissions from differently treated adjacent plots is a promising avenue to verify the effect of mitigation strategies at the field scale. In pursuing such an approach, it is crucial to accurately characterize the source area of the fluxes measured at each sampling point. Hence, a comprehensive footprint analysis method is required so that emission rates can be obtained for a specific field within a biochemically heterogeneous area. In this study, a footprint analysis method is developed to estimate the emission for an experiment where the flux of N<inf>2</inf>O is measured from several control and treated plots. The emission rate of an individual plot is estimated using an inverse footprint fraction approach where the footprint fractions are obtained from an analytical footprint model. A numerical solution for obtaining the background flux for such a multiplot measurement system is also provided. Results of the footprint analysis method are assessed, first, by comparing footprint fractions obtained from both an analytical footprint model and a “forward” simulation of a backward Lagrangian stochastic (bLs) model; and second, by comparing the emission rates of a control plot obtained from the footprint analysis method and from the “backward” simulation of the bLs model. It is found that the analytical footprint fractions compare well with the values obtained from the bLs model (correlation coefficient of 0.58 and 0.66 within p value <0.001). An average of 4.3 % of the measured fluxes is found to be contributed by sources outside the measured area and, excluding this outside area contribution to the measured flux, footprint corrected emission rates within the defined domain are found to increase by 2.1 to 5.8 % of the measured flux. Also, the proposed method of emission rate estimation is found to work well under a wide range of atmospheric stability.
SubjectsScience & Technology
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