Real-time and dynamic estimation of CO2 emissions from China’s lakes and reservoirs

Kun Sun; Junjie Jia; Shuoyue Wang; Yang Gao

doi:10.59717/j.xinn-geo.2023.100031

The Innovation Geoscience > 2023 Vol. 1 > No. 3 > 100031

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Real-time and dynamic estimation of CO₂ emissions from China’s lakes and reservoirs

1.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

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Corresponding author: gaoyang@igsnrr.ac.cn (Y.G.)
Received Date: 15 April 2023

Accepted Date: 08 November 2023

Published Online: 10 November 2023

The Innovation Geoscience 1(3), https://doi.org/10.59717/j.xinn-geo.2023.100031 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

PUBLIC SUMMARY
1. Lake CO₂ emissions were estimated using real-time water quality monitoring data.
  
  China’s total lake and reservoir CO₂ emission between 2021-2022 was 6.78 Tg C yr⁻¹.
  
  General environmental controls were pH, dissolved oxygen, and air temperature.

ABSTRACT

ABSTRACT

Lakes and reservoirs act as active carbon (C) reactors and regulators. Both play a crucial terrestrial ecosystem C balance role via carbon dioxide (CO₂) exchange processes across the water-air interface. It has previously been confirmed that CO₂ flux from lakes and reservoirs generally exhibits significant spatiotemporal heterogeneity. Nevertheless, spatiotemporal CO₂ flux variation has seldom been considered in global and regional CO₂ emission estimates from lakes and reservoirs. By accounting for spatiotemporal CO₂ flux and water surface area variability, we evaluated spatial and temporal CO₂ emission dynamics from China’s inland lakes and reservoirs using national real-time water quality monitoring data and machine learning (ML) models. Between 2021–2022, we estimated total C emission flux at 6.78 (±2.5) Tg C yr⁻¹, where seasonal and regional distribution both exhibited significant heterogeneity. Our state-of-the-art estimate is significantly lower than previous estimates of 7.9~25 Tg C yr⁻¹ from the 1980s to the 2010s. Water quality parameters (pH and dissolved oxygen [DO]) and climate factors (air temperature) were identified as the general environmental CO₂ flux controls. For the first time, this study clarifies the spatiotemporal patterns and drivers of CO₂ flux from China’s inland lakes and reservoirs, providing a more complete C budget picture of China’s aquatic ecosystems.

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Sun K., Jia J., Wang S., et al., (2023). Real-time and dynamic estimation of CO2 emissions from China’s lakes and reservoirs. The Innovation Geoscience 1(3), 100031. https://doi.org/10.59717/j.xinn-geo.2023.100031

Sun K., Jia J., Wang S., et al., (2023). Real-time and dynamic estimation of CO₂ emissions from China’s lakes and reservoirs. The Innovation Geoscience 1(3), 100031. https://doi.org/10.59717/j.xinn-geo.2023.100031

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Real-time and dynamic estimation of CO₂ emissions from China’s lakes and reservoirs

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Real-time and dynamic estimation of CO₂ emissions from China’s lakes and reservoirs