Dissolved organic carbon and its potential predictors in eutrophic lakes

Area of Science:

• Environmental Science
• Limnology
• Remote Sensing

Background:

• Accurate estimation of dissolved organic carbon (DOC) in lakes is vital for understanding their role in the global carbon cycle.
• Direct remote sensing of DOC is challenging due to optically inactive components.
• Developing reliable remote sensing methods for lake carbon content is a significant need for regional and global scales.

Purpose of the Study:

• To identify reliable water and environmental variables as proxies for remote sensing of lake DOC.
• To investigate the relationships between DOC and other variables in a large, shallow, eutrophic lake.
• To assess the potential of colored dissolved organic matter (CDOM) as a predictor for DOC concentrations.

Main Methods:

• Utilized the Boosted Regression Trees approach to determine the influence of various water and environmental variables on DOC.
• Analyzed seasonal and interannual variability of DOC and related parameters in Lake Võrtsjärv.
• Combined data from Lake Võrtsjärv with data from six other eutrophic lakes globally.

Main Results:

• In Lake Võrtsjärv, DOC and CDOM concentrations were high, with small seasonal and interannual variability.
• Chlorophyll a, total suspended matter, and Secchi depth showed correlations with DOC, suggesting potential for seasonal remote sensing.
• Transparency-related variables were identified as relevant proxies for long-term DOC changes.
• CDOM was not a consistent predictor of seasonal DOC in Lake Võrtsjärv due to variable coupling.
• Across multiple eutrophic lakes, CDOM emerged as the most powerful predictor of DOC.

Conclusions:

• While seasonal DOC dynamics in individual lakes can be complex, CDOM shows strong potential as a remote sensing proxy for DOC in eutrophic lakes globally.
• Transparency-related variables are important for long-term DOC monitoring.
• Further research can refine remote sensing techniques for lake carbon assessment.