Ebullition, the release of gas bubbles, is an important pathway of methane emission in many ecosystems, yet its high spatio–temporal variability makes it challenging to quantify. In this work, a methane-flux partitioning method based on scalar similarity in the wavelet domain is applied to eddy-covariance data collected at two flooded rice fields. Inspection of initial results indicates that several modifications are needed for robust ebullition detection. With these modifications, our objectives are to compare the original and modified methods, conduct a sensitivity analysis of the program’s empirical parameters, characterize the importance of ebullition in rice across growth stages, and identify the primary drivers of ebullition. The modified method’s ebullitive fluxes are significantly lower and show lower variance than those from the original method. Furthermore, the two methods produce distinct patterns of diel variation. While partitioning estimates show non-trivial sensitivity to the program parameters, this sensitivity is lower in magnitude than the random error in the ebullitive flux estimates. Ebullitive fluxes make up 9% of the total flux on average, with ebullition increasing in importance as plants develop. Ebullitive fluxes are best predicted by wind speed (negative effect), ecosystem respiration (positive effect), and sensible heat flux (positive effect), suggesting an indirect effect of plant-mediated transport, a link with temperature and methane production, and a potential effect of water column turnover, respectively. In addition to validating the method with independent ebullition observations, we recommend its application at more natural and managed wetlands to improve understanding of this highly variable transport pathway.

沸腾，即气泡的释放，是许多生态系统中甲烷排放的重要途径，但其高时空变异性使其难以量化。在这项工作中，基于小波域中标量相似性的甲烷通量划分方法应用于在两个淹水稻田采集的涡度协方差数据。对初始结果的检查表明，需要对稳健的沸腾检测进行一些修改。通过这些修改，我们的目标是比较原始方法和修改后的方法，对程序的经验参数进行敏感性分析，描述水稻不同生长阶段沸腾的重要性，并确定沸腾的主要驱动因素。修改后的方法的沸腾通量明显低于原始方法，并显示出较低的方差。此外，这两种方法产生了不同的模式变化。虽然分区估计显示对程序参数的非平凡敏感性，但这种敏感性在幅度上低于沸腾通量估计中的随机误差。沸腾通量平均占总通量的 9%，随着植物的发育，沸腾的重要性增加。风速（负效应）、生态系统呼吸（正效应）和感热通量（正效应）最好地预测沸腾通量，这表明植物介导的运输的间接影响，与温度和甲烷产生的联系，以及潜在的水柱周转的影响，分别。