The critical role of lake classification in refining global lake greenhouse gas emission estimates

Lakes play a critical role in estimating global greenhouse gas (GHG) emission budgets. Various human activities, such as agricultural practices, reclaimed water containing nutrients, and other point and non-point pollution, have led to significant nutrient loading and consequently elevated GHG emissions. As a result, research has increasingly focused on GHG flux patterns from these human-impacted lakes. However, this poses a challenge for global estimates: a significant mismatch may exist between the number of lakes with GHG flux measurements and the total number and area of lakes across lake types. Due to the contrasting lake formation processes and varying degrees of human disturbance, lakes can be classified into distinct types, such as human-impacted urban and non-urban lakes and natural permafrost and non-permafrost lakes. These types exhibit distinct characteristics in size and nutrient concentrations. Therefore, accounting for lake type is as essential as lake area for accurate global estimates. Yet, the extent to which lake classification influences global lake GHG emission estimates remains poorly understood. Here, we addressed this gap through a meta-analysis. We observed distinctive patterns in physicochemical properties and GHG measurements across lake types, and identified varied relationships between GHG fluxes and lake area among the four lake types. We classified global lakes into the four types described above based on the population-lake volume ratio or cropland-lake volume ratio, urban coverage, and permafrost coverage within 3 km of the lake. We then estimated GHG emissions from global lakes based on both lake type and size, demonstrating that global lakes emitted 813.0 (Q1–Q3: 575.7–1235.9) teragram CO₂-equivalents year^-1, which is one-third of the latest estimate without considering lake type. Human-impacted lakes contribute significantly to global lake GHG emissions, with disproportionately high emissions relative to their surface area. Our results provide new insights for improving the accuracy of global lake GHG emission estimates.