Abstract Lakes are an important type of surface water body, while their carbon emissions and the underlain mechanisms that potentially vary among different lakes remain highly uncertain. In this study, we modified the LAKE model and successfully applied it, along with multiple complementary machine learning methods, to Taihu Lake, a typical subtropical large shallow lake, to explore the carbon cycles and identify the corresponding influencing mechanisms and factors. Results showed that the modified LAKE model effectively captured both seasonal and interannual variations in lake water temperature, surface water chlorophyll‐a concentration, carbon dioxide (CO 2 ) concentration, CO 2 flux, and diffusion methane flux at different sites within the lake. Eutrophication and macrophytes reduced the CO 2 fluxes of Taihu Lake during 2007–2015 by 2.05 × 10 5 t C yr −1 and 4.56 × 10 4 t C yr −1 , respectively. In contrast, carbon input from inflow rivers and crab cultivation increased CO 2 fluxes by 5.33 × 10 4 t C yr −1 (2007–2015) and 3.44 × 10 3 t C yr −1 (2005–2008), respectively. Dissolved oxygen (DO) and chlorophyll‐a concentration exhibited spatially heterogeneous effects on surface CO 2 fluxes. DO was the main contributor in inflow‐affected zones, while chlorophyll‐a had a significant influence in algal‐dominated zones with limited watershed‐inflow influence. These mechanistic findings and the modified LAKE model could enhance scientific understanding and provide an effective tool for carbon emission management in large shallow lakes.
Zhu et al. (Wed,) studied this question.