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Global patterns, drivers and temperature sensitivity of methane and carbon dioxide emissions from lakes
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DOI：10.1016/j.jhydrol.2026.135552.png)
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Abstract
En 中文
Lakes are crucial carbon reservoirs in the Earth's carbon cycle, influencing greenhouse gas (GHG) emissions. However, systematic quantification of methane (CH4) and carbon dioxide (CO2) emissions across lakes has been limited. This study integrates GHG data from 1,562 lakes with machine learning models to assess spatiotemporal patterns and drivers of CH4 and CO2 emissions globally. Results showed that global lakes emitted 167.09 Tg & centerdot;CH4 & centerdot;yr(-1) and 0.66 Pg & centerdot;C & centerdot;yr(-1), with ebullition accounting for similar to 70% of total CH4 emissions. Lake emissions displayed a strong latitudinal pattern, with mid- to high-latitude lakes in the Northern Hemisphere contributing the most. Based on the Random Forest modeling and Partial Least Squares Path Modeling, climatic and human activities were identified as the dominant drivers. The Boltzmann-Arrhenius equation and linear mixed-effects model revealed a strong temperature dependence of CH4 emissions (E-TM = 0.76 eV), with the apparent activation energy of ebullitive fluxes (E-EM = 0.92 eV) notably higher than that of diffusive fluxes (E-DM = 0.66 eV), while CO2 exhibited a much weaker thermal response (ECO2 = 0.18 eV). Under the SSP585 (temperature-only scenario experiment), CH4 emissions can increase by as much as 19%. This study provides the global-scale quantification of the temperature dependence and multifactorial drivers of lake GHG emissions, highlighting the potential role of lake systems as climate accelerators under ongoing climate warming and intensified human activities. These results provide essential data constraints and methodological frameworks for enhancing global carbon budget assessments and GHG prediction models.
Keywords:
Global lakes
Methane
Carbon dioxide
Temperature sensitivity
Human activities
Machine learning
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6.3
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2.3W
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9.8W
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