Levy, P.E., Cowan, N., van Oijen, M., Famulari, D., Drewer, J., Skiba, U., (2017):
Estimation of cumulative fluxes of nitrous oxide: uncertainty in temporal upscaling and emission factors

European Journal of Soil Science 68, 400–411. doi:10.1111/ejss.12432

Abstract

Nitrous oxide (N₂O) is a greenhouse gas produced mainly by the microbial breakdown of agricultural fertilizer. ‘Emission factors’ (EFs, the fraction of nitrogen added that is released as N₂O) are based on flux chamber measurements following the application of fertilizer. These measurements are very variable in space and time so that EFs are often uncertain, but this is rarely quantified. We developed a method that simplifies the calculation of EFs, incorporates prior knowledge and quantifies the uncertainty with a Bayesian approach to fit the parameters of a lognormal model. We compared this with the standard method for interpolating, extrapolating and integrating fluxes of N₂O (trapezoidal integration). We verified both methods against process-based model output where the true integral was known and against eddy covariance data where the integral was estimated more accurately because of the greater spatial and temporal coverage. We used the process-based model to simulate flux chamber data and added a lognormal spatial distribution to the model output. The lognormal model performed better than the standard method, in terms of estimating the true underlying cumulative flux more accurately. Estimates based on chamber and eddy covariance data were sometimes substantially different, but with no clear systematic bias. The Bayesian approach with the lognormal model enabled us to combine both chamber and eddy covariance data to constrain cumulative fluxes. The standard trapezoidal method typically underestimates emission factors to some extent if fluxes are lognormally distributed in space. The Bayesian approach with the lognormal model is a robust method for quantifying the uncertainty in cumulative fluxes of N₂O.

Full Text: European Journal of Soil Science

Received: 25 Oct 2016 – Revised: 21 Feb 2017 – Accepted: 5 Mar 2017 – Published: 20 Jun 2017