Geospatial analysis of Alaskan lakes indicates wetland fraction and surface water area are useful predictors of methane ebullition

Abstract

Arctic-Boreal lakes emit methane (CH4), a powerful greenhouse gas. Recent studies suggest ebullition may be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed, but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial datasets of lake area, lake perimeter, permafrost, landcover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R² = 0.211), followed by lake surface area (adj. R² = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes may explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R² = 0.176) and temperature (adj. R² = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R² values are typical and informative for methane attribution studies. A multiple regression model combining LWF, area, and temperature performs best (adj. R² = 0.325). Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-Boreal lake methane emissions to readily available environmental variables.

A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double‐Counted Wetlands on Pan‐Arctic Lake Methane Emissions Estimates

“…Finer‐scale studies have identified other drivers including shoreline fluctuations (Hondula et al., 2021; Mullen et al., 2023), nutrient concentrations (Bastviken et al., 2004), and littoral zones (Larmola et al., 2004). While having modest correlation coefficients over broad scales (e.g., r 2 < 0.3, Savignano et al., 2023), lake area and temperature are nonetheless among the most predictive of available variables (Aben et al., 2022; Bastviken et al., 2004; Sanches et al., 2019; Yvon‐Durocher et al., 2014) and a minimum requirement for upscaling lake methane emissions over large, data‐poor areas such as the pan‐Arctic.…”