Recognition and prediction of dissolved organic matter (DOM) properties and greenhouse gas (GHG) emissions is critical to understanding climate change and the fate of carbon in aquatic ecosystems, but related data is challenging to interpret due to covariance in multiple natural and anthropogenic variables with high spatial and temporal heterogeneity. Here, machine learning modeling combined with environmental analysis reveals that urbanization (e.g., population density and artificial surfaces) rather than geography determines DOM composition and properties in lakes. The structure of the bacterial community is the dominant factor determining GHG emissions from lakes. Urbanization increases DOM bioavailability and decreases the DOM degradation index (Ideg), increasing the potential for DOM conversion into inorganic carbon in lakes. The traditional fossil fuel-based path (SSP5) scenario increases carbon emission potential. Land conversion from water bodies into artificial surfaces causes organic carbon burial. It is predicted that increased urbanization will accelerate the carbon cycle in lake ecosystems in the future, which deserves attention in climate models and in the management of global warming.
Keywords: Carbon cycle; Dissolved organic matter; Greenhouse gases; Lake; Machine learning; Urbanization.
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