In order to understand the spatiotemporal characteristics and key regulatory factors of greenhouse gas emissions from karst cascade reservoirs, four dammed reservoirs with different hydrological regulation characteristics in the Nanpanjiang-Hongshui River Basin were studied. The results show significant regional characteristics of the carbon dioxide (CO2) and methane (CH4) concentrations and emission fluxes from the reservoirs. The diffusive fluxes of CO2 and CH4 in the watershed were 4.98~260.11 mmol·m−2·d−1 and 0.01~0.27 mmol·m−2·d−1, respectively, of which the CO2 fluxes were higher than the average level of reservoirs in China. Estimation of the greenhouse gas emissions from the reservoirs indicated that the total greenhouse gas emissions of the basin system have clear spatial differences. Hydraulic load is the key factor regulating the greenhouse gas emission of karst dammed rivers in the Nanpanjiang-Hongshui River Basin. The CO2 emission flux of reservoirs was found to decrease first and then increase with the increase of hydraulic load, and there is a correlation between hydraulic load and methane concentration in the reservoirs. This study is of great significance for a comprehensive understanding of the greenhouse gas emission law and control mechanism of karst dammed rivers and can provide theoretical support and data reference for carbon emission assessment as well as carbon reduction in the karst reservoirs.
Analyzing variations in riverine dissolved organic carbon (DOC) concentration and flux is essential for understanding global carbon cycle processes and refining carbon budget estimations. Machine learning and big data analysis have become invaluable in this field. However, research on Chinese rivers is limited due to lack of long-term continuous observational data and non-uniform temporal distribution of key influencing factors. Consequently, mechanisms driving seasonal and long-term variations in riverine DOC and their influencing factors remain unclear. This study compared various machine learning methods using long-term, monthly DOC concentration data from the Yangtze River’s Xuliujing Station, as well as watershed characteristic data. Using the optimal model, we simulated monthly DOC concentration changes at Xuliujing Station from 2001 to 2020 and employed the SHAP (SHapley Additive exPlanations) method to analyze the impact of watershed characteristics on DOC concentration and flux. The findings demonstrated that the Random Forest algorithm yielded the highest accuracy, achieving an R² of 0.72 and an RMSE of 0.09 mg·L−1. Over the study period, DOC concentrations ranged from 1.24 to 2.27 mg·L−1, with a mean of 1.67 mg·L−1. Annual DOC flux varied between 0.93 and 2.41 Tg·a−1, averaging 1.46 Tg·a−1. Notably, the seasonal pattern of DOC concentration shifted from low during the flood season and high during the dry season to high levels in both seasons. This shift was primarily due to anthropogenic water regulation activities and changes in watershed ecosystem patterns. Over the long term, both DOC concentration and flux at Xuliujing Station have significantly increased, at rates of 0.026 mg·L−1·a−1 and 0.0025 Tg·a−1, respectively (both p<0.05). Human activities were the predominant driving factor, accounting for 54.1% of the changes in DOC concentration. This study provides valuable insights into the evolving patterns of DOC concentration and flux in the Yangtze River over recent decades and the mechanisms by which driving factors influence these changes. It also provides a novel perspective for the big data analysis of riverine carbon cycling.
Dissolved organic matter (DOM) plays an essential role in marine carbon cycling by modulating carbon sequestration and ecosystem dynamics through its degradation and transformation. As the largest marginal sea in China, the East China Sea (ECS) is a critical pathway for export of terrestrially derived DOM to the Pacific Ocean. This dynamic system is influenced by multiple environmental factors, including Yangtze River inputs, Kuroshio intrusion, and anthropogenic activities, which collectively contribute to the complex sources and transformations of DOM. However, the distribution of DOM and its underlying driving factors remain understudied in the ECS. Here, measurements of dissolved organic carbon (DOC) and its optical properties (absorbance of chromophoric DOM, CDOM; excitation-emission matrices on the fluorescent DOM, EEMs on the FDOM) were made in ECS water during a cruise in spring 2023 to provide insights into the mechanisms modulating DOM variability. The results showed strong variations in both quantity and quality of DOM, with the highest DOC concentration and absorption coefficient of CDOM (aCDOM(355)) close to the coast and decreasing offshore. DOC decreased from the surface to the bottom layer, whereas aCDOM(355) values showed an increasing trend. Four fluorescent DOM components were resolved by parallel factor analysis: two autochthonous protein-like components, C1 and C2; one terrestrial humic-like component, C3, and one marine humic-like component, C4. The fluorescence intensity of each component decreased as distance offshore increased. Elevated fluorescence intensities of C1 and C2 were observed in the surface and bottom layers, whereas minimum values were observed in the middle layer. The fluorescence intensities of C3 and C4 decreased as water depth increased. Principal component analysis (PCA) enabled differentiation of water samples following hydrodynamic gradient. The penetrating front in the ECS resulted in enhanced cross-shelf transport of terrestrially derived DOM, while phytoplankton blooms significantly altered the amount and compositions of DOM. Overall, the DOM in the ECS in spring 2023 was primarily of terrestrial origin, while autochthonous production, microbial transformation, and bottom resuspension were collectively responsible for its variability. This study provides a fundamental framework for characterizing DOM distribution patterns in the ECS during spring.
Fluorescent dissolved organic matter (FDOM) indices, sources, and their relationships with nutrients in the Yellow River Estuary, the Changjiang River Estuary and their adjacent seas in July 2024 were investigated through field sampling and laboratory analysis. The fluorescence index ranged from 2.71 to 4.45 in the Yellow River Estuary and its adjacent sea and from 3.22 to 5.69 in the Changjiang River Estuary and its adjacent sea. The biological index values were 1.75~3.48 and 1.49~4.81, respectively, indicating that autochthonous sources contributed most to the organic matter. The humification index of the Yellow River Estuary, the Changjiang River Estuary and their adjacent seas ranged from 0.22 to 0.85 and 0.10 to 1.14, respectively, suggesting weak humification and strong autogenic processes. Spatially, the fluorescence index and biological index increased while the humification index decreased toward the sea, indicating that organic matter is primarily driven by autochthonous production with minimal humification. The FDOM and fluorescence index were significantly higher in the Changjiang River Estuary than in the Yellow River Estuary, suggesting greater concentrations of organic matter and more substantial autochthonous sources in the Changjiang River Estuary and its adjacent sea. The fluorescence index and biological index were significantly negatively correlated with nutrient concentrations, while the humification index was positively correlated with nutrients. These results indicate that nutrient inputs significantly influence FDOM, further altering the organic matter composition and water quality in estuaries.
Coastal wetlands, recognized as vital blue carbon ecosystems, exhibit carbon sequestration functions that are strongly associated with plant functional traits. This study investigated the regulatory mechanisms of functional traits on carbon sequestration in the Yangtze River Estuary using two dominant salt marsh species: Phragmites australis and Spartina alterniflora. We quantified key functional traits, including morphological traits, photosynthetic parameters, and chlorophyll concentration, while simultaneously measuring ecosystem carbon fluxes (CO2 and CH4 emissions). Results demonstrate significant interspecific differences in functional traits: S. alterniflora exhibited superior leaf area index (LAI), chlorophyll concentration, photosynthetic efficiency, and CO2 assimilation capacity compared with P. australis. The photosynthetic capacity of P. australis was predominantly regulated by LAI, whereas that of S. alterniflora was mainly regulated by chlorophyll concentration. The CO2 fluxes showed a strong positive correlation with leaf traits and photosynthetic parameters. In contrast, CH4 emissions showed no association with leaf traits; however, they were influenced by morphological traits, such as aboveground biomass, plant height and plant density. These findings highlight that plant functional traits differentially mediate carbon sequestration pathways and greenhouse gas dynamics in coastal wetlands, providing critical insights for vegetation-based blue carbon management strategies.
In this study, we examined the synergistic effects of tidal inundation and vegetation type on litter decomposition and soil carbon dynamics in coastal wetlands, quantifying their influence on organic carbon (OC) fractions and providing a scientific foundation for enhancing wetland carbon sequestration. Specifically, we assessed how the co-effects of hydrological conditions and plant, species contribute to promoting the cycling of carbon. The study was conducted in the Chongming Dongtan wetland area of the Yangtze Estuary, in which we performed a 1-year in situ litter decomposition experiment, using Phragmites australis and Spartina alterniflora, to compare two tidal flood environments, namely, high (HM) and low (LM) elevational marshland areas. The rates of plant decomposition were determined based on the litter bag method, and soil samples were analyzed for the contents of soil organic carbon (SOC), particulate organic carbon (POC), mineral-associated organic carbon (MAOC), microbial biomass carbon (MBC), and dissolved organic carbon (DOC), using density fractionation and fumigation extraction techniques. The results revealed higher rates of decomposition in HM than in LM, with S. alterniflora and P. australis being characterized by rates of 0.0032 d–1 and 0.0021 d–1 in HM, respectively, which were 16% and 15% higher than those in LM at 360 days. Whereas initially, the decomposition of P. australis was more rapid in LM (p<0.01), the rates in HM were more pronounced during the latter stages of measurement. In HM, we detected significant elevations in the stocks of soil POC, MAOC, and SOC, associated with the efficient conversion of POC and MBC to MAOC, whereas in LM, we observed a lower DOC-to-MBC conversion and limited accumulation of POC. In addition, the type of vegetation was found to have an influence on carbon dynamics, with the levels of P. australis SOC peaking earlier in HM due to efficient conversion, whereas during the latter stages of the experiment, the levels of S. alterniflora SOC surpassed those of P. australis. Comparatively, in LM, the type of plant had a minimal influence on the dynamics of carbon fractions. Collectively, our findings provide evidence that by shaping the soil environment, tidal inundation can have a pronounced regulatory effect on the rates of plant decomposition and soil carbon stocks, with the species of plant modulating the conversion of carbon fractions in response to differing hydrological conditions. Compared with the marshland at lower elevations, that at higher elevations was established to have a greater carbon sequestration potential. On the basis of these observations, we recommend that wetland management should integrate hydrological and vegetation factors to optimize carbon storage, and further research should focus on the microbial mechanisms underlying these processes.
This study aimed to quantify the carbon stock contributed by reef-building oysters in the Dashentang oyster reef ecosystem, Tianjin. For the first time, the carbon stock of Crassostrea gigas (C. gigas) was evaluated using an inventory-based approach. Stepwise regression analysis was applied to examine the relationship between oyster carbon density and water quality parameters, thereby identifying the environmental factors that influence carbon density. In addition, a regression model was developed to predict the shell dry weight from shell height. Results revealed that in June and October 2024, the average total carbon density of C. gigas in the Dashentang Area of Tianjin was 34.37 t C/hm2 and 25.81 t C/hm2, respectively, corresponding to total carbon stocks of (6945.40±422.88) t C and (5214.01±458.33) t C, indicating higher carbon stock in June than in October. Water temperature and turbidity accounted for 77.6% of the total carbon density variability, thereby indicating that water temperature and turbidity influenced the carbon density in the study area. Furthermore, the goodness of fit of the power function ($ \ln y= $$ \ln0.002+2.126\ \ln x $) regression model for the shell dry weight and shell height demonstrated excellent fit (R2=0.958), confirming that shell dry weight can be reliably estimated from shell height for practical applications.
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