Improved gap filling approach and uncertainty estimation for eddy covariance N2O fluxes

Agricultural nitrous oxide (N2O) emissions comprise a majority of the global source of this powerful greenhouse gas. Mitigation approaches for reducing emissions are difficult to evaluate at appropriate field scales because of the substantial effort and expense associated with relatively new technology allowing eddy covariance measurements of N2O fluxes (F-N2(O)). Here we present a new approach for gap filling eddy covariance F-N2O, and estimating annual uncertainties for a temperate grazed grassland. We tested the potential of using one flux tower to evaluate emissions mitigation options in one paddock relative to an adjacent, unchanged paddock by partitioning data by source footprint contribution. Because of the complexity of spatiotemporal controls on F-N2O, we generated a large set of environmental variables and features as input for machine learning algorithms. Inputs were transformed using partial least squares (PLS) decomposition, isolating features with the greatest influence on F-N2O. PLS scores were fed to both a neural network (NN) and a locally-weighted k-nearest neighbours (kNN) regression. While the NN and kNN preformed similarly well, kNN regression accounted for the largest proportion of variance (52-72%) and resulted in the lowest bias for each of the three source footprint areas (full footprint and two separated adjacent paddocks, P53 and P54). Annual uncertainty estimates included random measurement uncertainty, accuracy and precision of the gap filling approach, and uncertainty associated with choice of threshold for atmospheric turbulence filtering and footprint contributions. Total N2O emissions for the full footprint, P53, and P54 were 7.4 +/- 0.35, 7.7 +/- 0.80, and 6.4 +/- 0.63 kg N2O-N ha(-1), respectively in Year 1, and 6.9 +/- 0.33, 7.3 +/- 0.63, and 6.7 +/- 0.63 kg N2O-N ha(-1), respectively in Year 2. These 95% confidence intervals on the annual F(N2O )suggest that we could detect differences of 10-15% between paddocks at this site when testing mitigation options.

Automated summary

This study introduces a new approach for evaluating emissions mitigation options by partitioning data by source footprint contribution. Machine learning algorithms are used in conjunction with eddy covariance measurements to estimate annual uncertainties for N2O emissions in temperate grazed grasslands.