Ebullition dominates methane fluxes from the water surface across different ecohydrological patches in a temperate freshwater marsh at the end of the growing season

The Importance of Lake Emergent Aquatic Vegetation for Estimating Arctic‐Boreal Methane Emissions

“…Four papers (Burger et al., 2016; Casper et al., 2000; Desrosiers et al., 2021; Dove et al., 1999) separately measured each of the three methane emission pathways, and most of the others focused on diffusion and/or plant‐mediated fluxes. An additional six (Huttunen et al., 2003; Jansen, Thornton, Cortés, et al., 2020; Juutinen et al., 2003; Larmola et al., 2004; Striegl & Michmerhuizen, 1998; Villa et al., 2021) measured diffusion and ebullition in both lake zones, but did not place the flux chamber over plants, thus not accounting for that pathway. One study (Bergström et al., 2007) did not provide open water values, which we estimated based on lake area via the relationship of Holgerson and Raymond (2016).…”

“…One key challenge to upscaling is the high within‐lake spatial variability of methane emissions. Total fluxes measured from vegetated (Villa et al., 2021) and shallow (Natchimuthu et al., 2016) zones can be statistically greater than those from open water and have been attributed to the majority of whole‐lake emissions (Saunois et al., 2020). Estimates derived from deep lake centers have been shown to underestimate total flux by 5%–78% in select lakes (Natchimuthu et al., 2016).…”

Detecting Hot Spots of Methane Flux Using Footprint‐Weighted Flux Maps

“…Thus, the spatial heterogeneity in methane emissions can be linked to the spatial heterogeneity in emergent vegetation cover. In open water areas and mudflats, the high spatial variability and the occurrence of ebullition events (Villa et al., 2021) can result in large intermittency and non‐stationarity that may bias the budgets of methane flux (Göckede et al., 2019). Ebullitive effects may be associated with hot spots of methane flux as they drive high variability in the measured methane fluxes (Zorzetto et al., 2021).…”

“…Gas molar mixing ratios were converted to molar densities using the ideal gas law. Where present, we accounted for ebullitive fluxes of CH 4 using the approach of Villa et al (2021). In short, an ebullition event is identified based on an excessively high increase in the concentration of the gas.…”

Causality Guided Machine Learning Model on Wetland Ch4 Emissions Across Global Wetlands

“…For example, temperature sensitivity scalars have been proposed based on observed CH 4 emissions (Yvon-Durocher et al, 2014). However, in situ observations reveal high variability and uncertainty in CH 4 emissions even with nearly identical environmental conditions (Chadburn et al, 2020;Granberg et al, 1997;Hemes et al, 2018;Koch et al, 2014;Rinne et al, 2018;Villa et al, 2021;Zona et al, 2016), implying much more complex functional relationships between CH emissions and environmental and biological factors. A few ecosystem models explicitly represent more of the underlying microbial, plant, and abiotic processes leading to wetland CH 4 emissions (e.g., ecosys (Grant et al, 2015;Grant et al, 2017a;Grant et al, 2017b), BAMS4 (Pasut et al, 2021), andJSBACH-methane (Castro-Morales et al, 2018)) and confirm that these nonlinear interactions should be considered to improve model predictions of methane emissions (Chang et al, 2019).…”