Department of Environmental Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.
Climate and Agriculture Group, Agroscope, Zurich, Switzerland.
PLoS One. 2018 Jul 26;13(7):e0200876. doi: 10.1371/journal.pone.0200876. eCollection 2018.
The static chamber approach is often used for greenhouse gas (GHG) flux measurements, whereby the flux is deduced from the increase of species concentration after closing the chamber. Since this increase changes diffusion gradients between chamber air and soil air, a nonlinear increase is expected. Lateral gas flow and leakages also contribute to non linearity. Several models have been suggested to account for this non linearity, the most recent being the Hutchinson-Mosier regression model (hmr). However, the practical application of these models is challenging because the researcher needs to decide for each flux whether a nonlinear fit is appropriate or exaggerates flux estimates due to measurement artifacts. In the latter case, a flux estimate from the linear model is a more robust solution and introduces less arbitrary uncertainty to the data. We present the new, dynamic and reproducible flux calculation scheme, kappa.max, for an improved trade-off between bias and uncertainty (i.e. accuracy and precision). We develop a tool to simulate, visualise and optimise the flux calculation scheme for any specific static N2O chamber measurement system. The decision procedure and visualisation tools are implemented in a package for the R software. Finally, we demonstrate with this approach the performance of the applied flux calculation scheme for a measured flux dataset to estimate the actual bias and uncertainty. The kappa.max method effectively improved the decision between linear and nonlinear flux estimates reducing the bias at a minimal cost of uncertainty.
静态箱法常用于温室气体 (GHG) 通量测量，通量是通过关闭箱后物种浓度的增加来推断的。由于这种增加改变了箱内空气和土壤空气之间的扩散梯度，因此预计会出现非线性增加。侧向气流和泄漏也会导致非线性。已经提出了几种模型来解释这种非线性，最近的是 Hutchinson-Mosier 回归模型 (hmr)。然而，这些模型的实际应用具有挑战性，因为研究人员需要为每个通量确定是否适合非线性拟合，或者由于测量误差而夸大通量估计。在后一种情况下，线性模型的通量估计是更稳健的解决方案，并且为数据引入的任意不确定性更小。我们提出了新的、动态的和可重复的通量计算方案 kappa.max，以在偏差和不确定性（即准确性和精密度）之间实现更好的折衷。我们开发了一种工具来模拟、可视化和优化任何特定静态 N2O 室测量系统的通量计算方案。决策过程和可视化工具在 R 软件的一个包中实现。最后，我们通过这种方法展示了所应用的通量计算方案在估计实际偏差和不确定性方面对测量通量数据集的性能。kappa.max 方法有效地改进了线性和非线性通量估计之间的决策，以最小的不确定性成本降低了偏差。
