Influence of hydrological features on CO2 and CH4 concentrations in the surface water of lakes, Southwest China: A seasonal and mixing regime analysis

Due to the large spatiotemporal variability in the processes controlling carbon emissions from lakes, estimates of global lake carbon emission remain uncertain. Identifying the most reliable predictors of CO2 and CH4 concentrations across different hydrological features can enhance the accuracy of carbon emission estimates locally and globally. Here, we used data from 71 lakes in Southwest China varying in surface area (0.01‒702.4 km2), mean depth (< 1‒89.6 m), and climate to analyze differences in CO2 and CH4 concentrations and their driving mechanisms between the dry and rainy seasons and between different mixing regimes. The results showed that the average concentrations of CO2 and CH4 in the rainy season were 23.9 ± 18.8 μmol L−1 and 2.5 ± 4.9 μmol L−1, respectively, which were significantly higher than in the dry season (10.5 ± 10.3 μmol L−1 and 1.8 ± 4.2 μmol L−1, respectively). The average concentrations of CO2 and CH4 at the vertically mixed sites were 24.1 ± 21.8 μmol L−1 and 2.6 ± 5.4 μmol L−1, being higher than those at the stratified sites (14.8 ± 13.4 μmol L−1 and 1.7 ± 3.5 μmol L−1, respectively). Moreover, the environmental factors were divided into four categories, i.e., system productivity (represented by the contents of total nitrogen, total phosphorus, chlorophyll a and dissolved organic matter), physicochemical factors (water temperature, Secchi disk depth, dissolved oxygen and pH value), lake morphology (lake area, water depth and drainage ratio), and geoclimatic factors (altitude, wind speed, precipitation and land-use intensity). In addition to the regression and variance partitioning analyses between the concentrations of CO2 and CH4 and environmental factors, the cascading effects of environmental factors on CO2 and CH4 concentrations were further elucidated under four distinct hydrological scenarios, indicating the different driving mechanisms between the scenarios. Lake morphology and geoclimatic factors were the main direct drivers of the carbon concentrations during the rainy season, while they indirectly affected the carbon concentrations via influencing physicochemical factors and further system productivity during the dry season; although lake morphology and geoclimatic factors directly contributed to the carbon concentrations at the vertically mixed and stratified sites, the direct effect of system productivity was only observed at the stratified sites. Our results emphasize that, when estimating carbon emissions from lakes at broad spatial scales, it is essential to consider the influence of precipitation-related seasons and lake mixing regimes.