Carbon dioxide emission forecasting for medium and large reservoirs in China

ZHANG Yi; XU Qiao; YIN Xin'an; YANG Baiheng; SUN Yue; GUAN Xinran; WU Zijing; SUN Chuqi

doi:10.13205/j.hjgc.202605009

Volume 44 Issue 5

May 2026

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2026 > 44(5): 92-102

ZHANG Yi, XU Qiao, YIN Xin'an, YANG Baiheng, SUN Yue, GUAN Xinran, WU Zijing, SUN Chuqi. Carbon dioxide emission forecasting for medium and large reservoirs in China[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(5): 92-102. doi: 10.13205/j.hjgc.202605009

Citation:

ZHANG Yi, XU Qiao, YIN Xin'an, YANG Baiheng, SUN Yue, GUAN Xinran, WU Zijing, SUN Chuqi. Carbon dioxide emission forecasting for medium and large reservoirs in China[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(5): 92-102. doi: 10.13205/j.hjgc.202605009

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Carbon dioxide emission forecasting for medium and large reservoirs in China

doi: 10.13205/j.hjgc.202605009

State Key Laboratory of Regional Environment and Sustainability, School of Environment, Beijing Normal University, Beijing 100875, China

Received Date: 2026-01-09
Available Online: 2026-06-06

Abstract

Abstract

Reservoir carbon dioxide （CO₂） emission forecasting is a key component of the global carbon cycle. Most existing prediction studies are based on machine learning models， where data from large-， medium-， and small-sized reservoirs are used together for model calibration. Neither the model calibration nor the emission forecasting is stratified by reservoir capacity level， thereby ignoring the impact of reservoir size on the carbon cycle mechanism. To address this problem， this study abandoned the traditional full-scale hybrid modeling strategy and constructed a training dataset specifically for large- and medium-sized reservoirs. Using data from large- and medium-sized reservoirs in China， this study established a CO₂ emission forecasting model， thoroughly explored the spatiotemporal evolution of CO₂ flux across different river basins， and quantitatively identified the leading driving factors. The results showed that CO₂ flux of reservoirs in China exhibited significant latitudinal zonation， with values in low-latitude areas in the south significantly higher than those in high-latitude areas in the north. CO₂ flux was significantly correlated with reservoir age， latitude， and total phosphorus content. Among these， total phosphorus content was the most strongly correlated driving factor， showing a positive exponential correlation with CO₂ flux. However， physical parameters such as reservoir area， pre-impoundment submersion ratio， and average water depth showed no significant correlation with CO₂ flux at the national scale， and the dominant influencing factors varied heterogeneously across different basins. The findings of this study provide a theoretical basis for clarifying the carbon emission characteristics of large- and medium-sized reservoirs in China offer scientific support for the management of carbon sinks in reservoirs.
- large-and medium-sized reservoirs in China,
- CO₂ flux,
- influencing factors,
- spatial distribution,
- neural network

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References(29)

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