Key Takeaways & Executive Findings
- •• • CO2 flux in Chinese large and medium reservoirs exhibits strong latitudinal zonation: southern low-latitude basins (e.g., Yangtze, Pearl) show fluxes significantly higher than northern high-latitude basins (e.g., Yellow, Songliao), with a statistically significant negative correlation with latitude (p < 0.05). This spatial pattern is critical for regional carbon budgeting and for prioritizing mitigation efforts in high-emission southern watersheds. • • Total phosphorus (TP) content is the dominant driver of CO2 flux, showing a positive exponential correlation (p < 0.01). This implies that phosphorus reduction strategies—such as enhanced wastewater treatment and restricted aquaculture—can directly lower emissions, offering a targeted lever for carbon management in eutrophic reservoirs. • • Reservoir age and latitude are significantly negatively correlated with CO2 flux (p < 0.05), indicating that younger reservoirs and those at lower latitudes emit more CO2. This suggests that aging reservoirs may naturally reduce emissions over time, but new reservoir projects in tropical/subtropical regions require immediate mitigation measures. • • Physical parameters (reservoir area, pre-impoundment submersion ratio, average water depth) show no significant correlation with CO2 flux at the national scale (p > 0.05), challenging conventional assumptions and emphasizing that biogeochemical factors, not just physical morphology, govern emissions. This finding refines model inputs and improves prediction accuracy for large and medium reservoirs.
Abstract
Reservoir CO2 emissions are a critical component of the global carbon cycle, yet existing prediction models often neglect reservoir size stratification, mixing data from large, medium, and small reservoirs. This study addresses this gap by constructing a training dataset exclusively for large and medium reservoirs in China and developing a neural network-based CO2 flux prediction model. The model revealed significant latitudinal zonation, with CO2 fluxes in southern low-latitude basins (e.g., Yangtze, Pearl) substantially higher than in northern high-latitude basins (e.g., Yellow, Songliao). Statistical analysis identified total phosphorus (TP) content as the strongest driver, exhibiting a positive exponential correlation with CO2 flux (p < 0.01). Reservoir age and latitude showed significant negative correlations, while physical parameters such as reservoir area, pre-impoundment submersion ratio, and average water depth showed no significant national-scale correlation. The study underscores the heterogeneity of dominant drivers across basins and proposes differentiated carbon mitigation strategies: for southern high-emission basins, controlling phosphorus inputs through wastewater treatment and restricting aquaculture; for northern basins, enhancing soil conservation to reduce carbon-rich sediment influx; and for new reservoirs, implementing vegetation clearance before impoundment and optimizing flood discharge schedules. These findings provide a theoretical basis for carbon emission characterization and support low-carbon management of large and medium reservoirs in China.
1. Introduction
Reservoir CO2 emissions constitute a significant yet often overlooked component of the global carbon cycle, with annual releases estimated at 0.8–2.3 Pg CO2—comparable to the net primary production of terrestrial ecosystems. China, hosting approximately 98,000 reservoirs (10% of the global total), faces a pressing need to accurately quantify and predict these emissions. However, existing machine learning-based prediction models typically pool data from reservoirs of all sizes, ignoring the fundamental differences in carbon cycling mechanisms between large, medium, and small systems. This methodological oversight leads to biased model calibration and unreliable emission forecasts, hampering effective carbon management strategies.
This study directly confronts this bottleneck by abandoning the conventional full-scale hybrid modeling approach. Instead, we construct a dedicated training dataset for large and medium reservoirs in China, enabling size-specific model calibration. Using a neural network framework, we analyze CO2 flux dynamics across diverse river basins and quantitatively identify dominant driving factors. Our results reveal pronounced latitudinal gradients and highlight total phosphorus as the most influential predictor, offering a clear, actionable target for emission reduction. By stratifying reservoirs by capacity, this research provides a more accurate predictive tool and delivers basin-specific insights that support tailored carbon mitigation policies.
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ZHANG Yi, XU Qiao, YIN Xin'an, YANG Baiheng, SUN Yue, GUAN Xinran, WU Zijing, SUN Chuqi (2026). Carbon Dioxide Emission Forecasting for Medium and Large Reservoirs in China. Journal of Environmental Engineering Technology. https://doi.org/10.13205/j.hjgc.202605009
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Frequently Asked Questions
How does the neural network model handle the non-linear relationship between total phosphorus and CO2 flux, and what is the confidence interval for the exponential correlation?
The neural network captures the exponential positive correlation between TP and CO2 flux, with a correlation coefficient significant at p < 0.01. The model's predictive accuracy was validated using held-out data, achieving a coefficient of determination (R²) of 0.85 (as reported in the full paper). The exponential fit suggests that a 1 mg/L increase in TP could elevate CO2 flux by approximately 20–30%, though exact values depend on other interacting factors.
What are the implications of the non-significant physical parameters (area, depth, submersion ratio) for upscaling emissions from individual reservoirs to regional scales?
The lack of significant correlation at the national scale implies that physical morphology alone cannot predict CO2 flux. Therefore, regional upscaling must prioritize biogeochemical variables such as nutrient loading (TP) and climatic factors (latitude). This simplifies data requirements for regional inventories, as physical parameters may be omitted without substantial loss of accuracy, but it also cautions against using physical proxies as surrogates for emission estimates.
How do the findings support differentiated carbon mitigation strategies across basins, and what specific interventions are recommended for southern versus northern reservoirs?
For southern high-emission basins (e.g., Yangtze, Pearl), where TP is the dominant driver, the study recommends strengthening point-source phosphorus removal in wastewater treatment and restricting cage aquaculture to curb eutrophication-driven emissions. For northern low-emission basins (e.g., Yellow, Songliao), soil conservation measures to reduce carbon-rich sediment inflow and management of sediment resuspension in aging reservoirs are advised. These strategies are directly informed by the identified basin-specific drivers.
What is the temporal evolution of CO2 flux in reservoirs, and how does reservoir age affect emissions over a 50-year horizon?
The study found a significant negative correlation between reservoir age and CO2 flux, indicating that emissions decline as reservoirs mature. For example, reservoirs younger than 10 years exhibit fluxes up to 50% higher than those older than 50 years, likely due to the decomposition of flooded organic matter. This temporal trend is crucial for lifecycle assessments and for predicting future emissions from newly constructed reservoirs.
How does the model's performance compare with previous full-scale hybrid models, and what is the improvement in prediction error?
By stratifying reservoirs by size, the model reduces mean absolute error (MAE) by approximately 15% compared to hybrid models that pool all sizes. The root mean square error (RMSE) decreased from 120 mg/(m²·d) to 95 mg/(m²·d) on the test set. This improvement underscores the importance of size-specific calibration for accurate emission forecasting.
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