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Article

Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China

by
Ya Zhang
1,
Li Liu
1,*,
Hua Luo
1,
Wei Wang
2 and
Peng Li
1
1
Hubei Geological Bureau, Wuhan 430034, China
2
Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(22), 3169; https://doi.org/10.3390/w16223169
Submission received: 20 September 2024 / Revised: 1 November 2024 / Accepted: 4 November 2024 / Published: 6 November 2024
(This article belongs to the Special Issue Biodiversity Conservation of Aquatic Organisms, Especially Aquatic Plants, and Aquatic Ecosystem Health)
Browse Figures
Figure 1
<p>Sketch map of the study area.</p> "> Figure 2
<p>The monthly variation of environmental factors in the Chenhu wetland during the year 2022. (<b>a</b>) Air temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>), soil temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> </mrow> </msub> </mrow> </semantics></math>), and average <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> in Wuhan from 2011 to 2021. (<b>b</b>) Wind speed (WS), photosynthetically active radiation (PAR). (<b>c</b>) Relative humidity (RH), vapor pressure deficit (VPD). (<b>d</b>) Soil water content (SWC), total precipitation (PPT), and average PPT in Wuhan from 2011 to 2021.</p> "> Figure 3
<p>Diurnal variation of NEE in the Chenhu Wetland during 2022 ((<b>a</b>) growing season; (<b>b</b>) non-growing season).</p> "> Figure 4
<p>The daily variation of NEE on an annual basis (<b>a</b>) and the seasonal variation of monthly total NEE (<b>b</b>) for the Chenhu wetland during the year 2022.</p> "> Figure 5
<p>Comparison of annual NEE in subtropical wetlands.</p> "> Figure 6
<p>Path analysis between main environmental factors and NEE. χ<sup>2</sup> = 0.5, <span class="html-italic">p</span> = 0.46 &gt; 0.05, Degrees of freedom = 1. NFI = 0.999, CFI = 1.000, root mean square error of approximation (RMSEA) = 0.</p> "> Figure 7
<p>Scatter plots of daytime NEE against PAR from April to September 2022. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.</p> "> Figure 8
<p>Scatter plots of daytime NEE against PAR under different VPD during the growth season. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.</p> "> Figure 9
<p>Scatter plots of NEE against T<sub>a</sub> of Chenhu wetland during the night in the growing season (<b>a</b>) and throughout the day in the non-growing season (<b>b</b>). The fitting results and correlation coefficients between NEE and T<sub>a</sub> are also shown.</p> ">
Review Reports Versions Notes

Abstract

:
Carbon dioxide (CO2) flux measurements were conducted throughout the year 2022 utilizing the eddy covariance technique in this study to investigate the characteristics of carbon fluxes and their influencing factors in the Chenhu wetland, a representative subtropical lake-marsh wetland located in the middle reaches of the Yangtze River in China. The results revealed that the mean daily variation of CO2 flux during the growing season exhibited a U-shaped pattern, with measurements ranging from −12.42 to 4.28 μmolCO2·m−2·s−1. The Chenhu wetland ecosystem functions as a carbon sink throughout the growing season, subsequently transitioning to a carbon source during the non-growing season, as evidenced by observations made in 2022. The annual CO2 absorption was quantified at 21.20 molCO2·m−2, a figure that is lower than those documented for specific subtropical lake wetlands, such as Dongting Lake and Poyang Lake. However, this measurement aligns closely with the average net ecosystem exchange (NEE) reported for wetlands across Asia. The correlation between daytime CO2 flux and photosynthetically active radiation (PAR) can be accurately represented through rectangular hyperbola equations throughout the growing season. Vapor pressure deficit (VPD) acts as a constraining factor for daytime NEE, with an optimal range established between 0.5 and 1.5 kPa. Furthermore, air temperature (Ta), relative humidity (RH), and vapor pressure difference (VPD) are recognized as the principal determinants affecting NEE during the nocturnal period. The association between Ta and NEE during the non-growing season conforms to the van’t Hoff model, suggesting that NEE increases in response to elevated Ta during this timeframe.

1. Introduction

Wetlands, as one of the most significant carbon reservoirs within terrestrial ecosystems, play a crucial role in the global carbon cycle. Despite occupying merely 6% of the Earth’s land area, wetlands are responsible for sequestering approximately 20% to 30% of the total soil carbon globally. Their functionality is essential for stabilizing the global climate and mitigating the impacts of the greenhouse effect [1,2]. In recent years, climate change and anthropogenic activities have resulted in substantial reductions in wetland areas and alterations in land use patterns [3], as well as significant changes in the natural carbon cycling processes within ecosystems [4]. The classification of wetlands as either carbon sources or carbon sinks serves as a critical metric for evaluating the effectiveness of wetland ecological restoration efforts and the capacity of these ecosystems to contribute to climate change mitigation [5]. However, considerable uncertainties persist regarding the carbon and water budgets of wetland ecosystems due to a multitude of individual and interactive factors, including geographical location, vegetation types, soil conditions, meteorological factors, and human activities [6]. For example, the alpine wetland ecosystem of Qinghai Lake consistently functions as a “carbon sink” throughout the year, while the peatland at Yikulanwa Station, located south of Qinghai Lake, is characterized as a “carbon source” [2,7]. Therefore, it is imperative to investigate and analyze the carbon exchange characteristics and carbon budget mechanisms of wetland ecosystems across various regions and types. Monitoring carbon flux within these ecosystems is a critical method for determining whether they function as carbon sources or sinks, and it is also a vital component in understanding the carbon cycle within terrestrial ecosystems [7].
The middle reaches of the Yangtze River constitute a significant region characterized by the presence of lake and reservoir wetlands. This area exhibits the highest density of wetlands recognized as important at both international and national levels in China [8]. Furthermore, it is one of the regions in China that demonstrates heightened sensitivity to extreme rainfall as a consequence of climate change [9]. In recent years, the region has experienced a notable increase in temperature, accompanied by an uneven distribution of precipitation, a marked reduction in wetland area, and the deterioration of wetland ecological functions [10]. These alterations affect the biophysical and chemical processes within wetlands, thereby influencing the carbon cycle both in the middle Yangtze reaches and on a global scale. Currently, domestic researchers have undertaken investigations into carbon flux within various wetland ecosystems, including alpine wetlands [7,11], arid desert wetlands [12,13,14], estuary delta wetlands, coastal wetlands [5,15,16,17], the Sanjiang Plain swamp wetland [18], the Shennongjia subalpine wetland [19], and constructed wetlands [20]. However, while some studies have focused on carbon flux in wetland ecosystems along the middle reaches of the Yangtze River, the majority of these investigations have primarily concentrated on lake wetlands, such as those found located in Hongze Lake [21], Taihu Lake [22], Poyang Lake [23], and Dongting Lake [24]. Marsh wetlands constitute a significant component of the wetland ecosystem in the middle Yangtze reaches, possessing considerable ecological value and playing a crucial role in sustaining the environmental functions of these wetlands. Nevertheless, there is a notable lack of direct measurements concerning carbon exchange between the atmosphere and the marsh wetland ecosystem, resulting in limited evidence to elucidate the dynamics of carbon exchange and its relationship with environmental controls within this ecosystem. Therefore, to establish a scientific foundation for sustainable management practices and to facilitate accurate predictions of the carbon budgets across various wetland types in the middle Yangtze reaches, it is essential to investigate the temporal dynamics and the underlying biophysical variables that drive carbon fluxes in these ecosystems.
As one of China’s internationally important wetlands, the Chenhu Wetland represents the largest freshwater lake-marsh wetland in the middle reaches of the Yangtze River. It is recognized as one of the most well-preserved wetlands globally at this latitude, characterized by its intact ecosystem and serving as a crucial representative site on the northern bank of the Yangtze River [25,26]. This study, conducted at the Chenhu Wetland Field Comprehensive Observation Research Station using eddy covariance methodology, examined the dynamic variations and environmental regulatory mechanisms that influence net ecosystem CO2 exchange. The findings provide essential data for understanding carbon cycling within marsh wetland ecosystems in the middle reaches of the Yangtze River, thereby facilitating the assessment of carbon budgets and enhancing the carbon sequestration capabilities of wetland ecosystems.

2. Materials and Methods

2.1. Site Description

The Chenhu wetland flux tower is strategically located at the intersection of the core and buffer area of the Chenhu Wetland Provincial Nature Reserve (Figure 1). This area is characterized by a mixed herbaceous community predominantly composed of Phragmites communis and Miscanthus sacchariflorus, which provides an excellent habitat for a diverse array of migratory and resident avian species. In recognition of the ecological significance of this site, the local authorities have instituted a ban on all agricultural activities within the reserve. The Chenhu wetland is situated in the southwestern region of the Caidian District in Wuhan City. This area is characterized as a delta formed by the confluence of the Yangtze and Hanjiang Rivers. The geographical coordinates of the wetland are 30°15′10″ to 30°24′44″ N and 113°44′07″ to 113°55′39″ E with an elevation ranging from 17.5 to 21 m above sea level. The region experiences a northern subtropical continental monsoon climate, which is characterized by cold winters and hot summers, concurrent rainfall and heat during the same season, four distinct seasons, abundant precipitation, ample sunlight, and an extended frost-free period. The mean annual temperature is recorded at 16.5 °C, with an average of 2112 h of sunshine per year. Annual precipitation is measured at 1250 mm, while relative humidity ranges from 62% to 82%. The dryness index falls between 0.5 and 1.0. The reserve is characterized by low-lying terrain and marshland, formed from alluvial and silt deposits originating from river systems. The predominant soil types in this area include alluvial soil and paddy soil, with a limited presence of meadow soil found along the lake’s shoreline.
The water levels in the Chenhu wetland exhibit considerable seasonal variation. During the rainy season, which occurs from April to July, the accumulation of precipitation and flooding results in the interconnection of ditches, rivers, and lakes within the reserve. In contrast, during the dry season, from October to March, only the central portion of the lake and certain channels maintain a water surface, while the majority of the wetland transitions into a marsh meadow. This dynamic fosters the establishment of a wetland ecosystem that links shallow lake environments with marsh meadows. Within this ecosystem, the emergent vegetation, such as reeds and water chestnuts, is predominant, followed by submerged plant species. The distribution of vegetation illustrates a distinct ecological zonation, transitioning from submerged plants in the center of the lake to emergent plants and shoreline wetland vegetation at the lake’s periphery.

2.2. Data Sources

An eddy covariance (EC) system was implemented on a flux tower to facilitate continuous measurements of carbon dioxide (CO2) flux over a wetland ecosystem. This system consisted of a closed-path CO2/H2O gas analyzer (CPEC310, Campbell Inc., Southaven, MS, USA) and a sonic anemometer (CSAT3A, Campbell Inc., Southaven, MS, USA). Fluctuations in wind speed, air temperature, carbon dioxide concentrations, water molar densities, and standard meteorological parameters were measured at an elevation of 8 m with a sampling frequency of 10 Hz, utilizing a data logger (CR6, Campbell Inc., Southaven, MS, USA). The measurement system for conventional meteorological elements included a photosynthetically active radiation sensor (LI190R-L, Li-Cor Inc., Lincoln, NE, USA) and a four-component net radiation sensor (SN-500-SS, Apogee Inc., Austin, TX, USA), both positioned at a height of 8 m to evaluate photosynthetically active radiation and net radiation. Concurrent measurements of air temperature, humidity, precipitation, wind speed, and wind direction were conducted at a consistent height. Soil parameters assessed included soil temperature, soil water potential, and soil heat flux at depths of 20 cm and 50 cm. Average values were generated and recorded in the data logger every thirty minutes. The real-time observational data underwent comprehensive correction through the online processing program EasyFlux-DL, utilized by the data collector, prior to output.

2.3. Data Processing

The original data obtained from the eddy covariance system may contain outliers or exhibit missing values due to various factors, including system maintenance, voltage instability, and power failures. The flux data were processed in accordance with the standard procedures for flux data processing as recommended by ChinaFLUX. It is essential to emphasize that the calculated CO2 flux (Fc) values underwent rigorous quality control measures, which included (1) the elimination of data recorded during equipment malfunctions; (2) the exclusion of daytime data that deviated from the expected trend of light radiation; (3) the replacement of flux data classified with a quality grade of 9, indicating poor data quality; (4) the removal of nighttime data exhibiting negative NEE; (5) the exclusion of nighttime data collected under conditions of weak turbulence, specifically with wind speeds (u*) less than 0.1 m·s−1; and (6) the exclusion of NEE values that fell outside the instrument’s measurement range, specifically when Fc exceeded −2.64 to 1.32 mg·m−2·s−1. Following the data processing and quality control procedures, 86.8% of the daytime and 43.8% of the nighttime Fc data were deemed suitable for further analysis.
In the process of interpolating missing data, a linear interpolation method that utilizes adjacent measurement data was employed for gaps with a duration of less than 2 h. Conversely, for data gaps that exceeded 2 h, the Michaelis–Menten model [27] was applied to estimate the missing NEE during the growing season:
N E E = − α P m a x · P A R α · P A R + P m a x + R e c o , d a y
where PAR represents the photosynthetically active radiation (μmol·m−2·s−1), α denotes the ecosystem’s apparent initial radiation use efficiency (μmolCO2·μmol−1), P m a x refers to the maximum photosynthetic capacity of the ecosystem (μmol CO2·m−2·s−1), and R e c o , d a y denotes the daytime ecosystem respiration (μmol CO2·m−2·s−1).
The absent nighttime NEE was estimated using an exponential function [28] that characterizes the relationship between soil respiration and temperature:
R e c o , n i g h t = a · e b T
where R e c o , n i g h t refers to nighttime Fc (μmol·m−2·s−1), T is soil temperature (° C), the coefficient a is the intercept of R e c o , n i g h t when the temperature is zero (μmol CO2·m−2·s−1), and the coefficient b represents the temperature sensitivity of R e c o , n i g h t .
The integral daily NEE was partitioned into ecosystem respiration ( R e c o ) and gross primary productivity (GPP) utilizing Equations (3) and (4):
R e c o = R e c o , d a y + R e c o , n i g h t
G P P = R e c o − N E E
where daily ecosystem respiration Reco is the sum of daytime respiration ( R e c o , d a y ) and nighttime respiration ( R e c o , n i g h t ). The nighttime net exchange flux is considered indicative of nighttime ecosystem respiration. Within the framework of this study, a negative value of NEE is traditionally interpreted as the wetland sequestering CO2 from the atmosphere, while a positive NEE value signifies the release of CO2 from the wetland into the atmosphere.

3. Results and Discussion

3.1. Meteorological Conditions

Figure 2 illustrates the temporal fluctuations in the monthly mean air temperature ( T a ), soil temperature ( T s ) at a depth of 20 cm, wind speed (WS), photosynthetically active radiation (PAR), relative humidity (RH), vapor pressure difference (VPD), soil water content (SWC) at a depth of 20 cm, and total precipitation (PPT) recorded at the Chenhu wetland station throughout the year 2022. The parameters of T a , T s , PAR, VPD, SWC, and PPT exhibited significant seasonal variation, characterized by a distinct single peak trend. However, the months in which these peaks occurred varied among the parameters. Specifically, the highest values for T a , T s , PAR, and VPD were recorded in August, whereas the peak values for SWC and PPT were observed in June and July, respectively.
The average T a in August was recorded at 31.57 ± 4.10 °C, representing the highest monthly average, while the average T a in January was the lowest at 6.06 ± 2.83 °C (Figure 2a). These temperature values exceeded the monthly average temperatures documented in Wuhan from 2011 to 2021 as per the data provided in the Wuhan Statistical Yearbook. T s   exhibited a similar trend to that of T a , with maximum recorded values of 28.31 ± 0.45 °C, and consistently remained lower than T a from March to August. The PAR levels were observed to be elevated during the summer months in comparison to other seasons (see Figure 2b). In 2022, the total annual rainfall was measured at 571.00 mm, which is significantly below the average annual precipitation of 1320.46 mm recorded in Wuhan from 2011 to 2021, as reported by the Hubei Provincial Water Conservancy Department. The majority of the rainfall occurred in March and April (refer to Figure 2d). Wind speed values were notably higher in March, April, June, and July (Figure 2b), which can be primarily attributed to large-scale atmospheric circulation patterns. Wind speeds exceeding 3.0 m·s−1 had an occurrence frequency of 25.3% throughout the year, with a predominant concentration observed from March to August. The RH ranged from 58.49% to 80.38% (Figure 2c), which is lower than the monthly mean RH recorded in Wuhan from 1961 to 2015, with the exception of January [29]. The SWC values were particularly low (<30%) during January and from August to December (Figure 2d), but exhibited a significant increase from February to March, peaking at 43.23% in June. The seasonal variation in SWC closely mirrored that of rainfall, albeit with a temporal lag. This lag can be attributed to the water absorption by the root systems of vegetation, which inhibits a rapid increase in soil water content [7].

3.2. Dynamics of CO2 Flux over Various Temporal Scales

3.2.1. Diurnal Variations in NEE

Utilizing data collected throughout the year 2022, the hourly flux measurements were averaged on a monthly basis from 00:00 to 24:00 (Beijing Standard Time) during both the growing season (April to September) and the non-growing season (January to March and October to December). The diurnal variation of NEE during these two distinct periods is depicted in Figure 3. In the growing season, there was a significant increase in CO2 exchange activity, characterized by enhanced CO2 absorption during daylight hours and CO2 release at night, in comparison to the non-growing season. NEE values ranged from −12.42 ± 8.00 to 4.28 ± 5.45 μmolCO2·m−2·s−1. Conversely, during the months of January, February, and December, the wetland ecosystem exhibited characteristics of a weak CO2 source, as indicated by positive flux values, with NEE ranging from −1.26 ± 2.67 to 1.56 ± 3.53 μmolCO2·m−2·s−1.
Throughout the growing season, the average NEE for each month indicated that the ecosystem functioned as a source of CO2 during nighttime and as a sink, characterized by negative flux values, during daytime, as illustrated in Figure 3. The average diurnal variation of CO2 flux during the growing season exhibited a U-shaped curve. Although the diurnal patterns of NEE across various growth stages displayed similar shapes, they differed significantly in amplitude. Specifically, the range of NEE variation in May was recorded at 15.76 μmolCO2·m−2·s−1, while in September it was 6.55 μmolCO2·m−2·s−1. These findings are consistent with the variations in carbon flux observed in other wetland ecosystems during the growing season, as reported in prior research [30,31]. In contrast, during the non-growing season, the CO2 flux exhibited minimal variation at night, with some fluctuations occurring during the daytime. In summary, from March to October, NEE displayed negative values during daylight hours, indicating net carbon absorption. Conversely, it exhibited positive values at night, signifying net carbon release. NEE values were recorded as less than zero following sunrise (when PAR exceeded 200 μmol·s−1·m−2), indicating that the photosynthetic activity of the reeds surpassed the combined rates of autotrophic respiration and soil heterotrophic respiration. Consequently, the wetland ecosystem functioned as a carbon sink during this period. Peak NEE values were recorded between 11:00 and 14:00 h across various growth stages. Subsequently, the net CO2 absorption displayed a pattern of decreasing fluctuations. Notably, between 17:00 and 18:30, NEE transitioned from negative to positive, reflecting a shift from a carbon sink to a carbon source as photosynthesis ceased at night. During nocturnal hours, the CO2 flux was positive, attributed to the respiration processes of both plants and soil. Variations in radiation exposure resulted in differing durations of the negative NEE values throughout the months. The average duration of carbon sink activity from March to October was recorded as 9.5 h, 10.5 h, 11.5 h, 11 h, 9.5 h, 9 h, and 8.5 h, respectively, demonstrating an initial increase followed by a subsequent decrease in duration. As the leaf area of vegetation expanded, both the photosynthetic efficiency and water use efficiency of the plants improved, leading to an enhanced carbon sequestration capacity of the ecosystem during periods of vigorous plant growth [7]. Conversely, in January, February, and December, the plants experienced dormancy, resulting in predominantly positive NEE values throughout the day.

3.2.2. Seasonal Variations in NEE

As illustrated in Figure 4a, the NEE exhibited significant negative values from April to September, particularly during the summer months. In contrast, NEE values predominantly approached zero during the winter, early spring, and late autumn, a phenomenon largely attributable to the surface vegetation. The mean monthly total NEE was recorded at −1.77 molCO2·m−2·month−1, with considerable fluctuations ranging from −7.78 molCO2·m−2·month−1 in May to 2.32 molCO2·m−2·month−1 in January. Throughout the growing season, spanning from April to September, the cumulative monthly respiratory losses of CO2 reached a peak of 34.06 molCO2·m−2, while approximately 60.65 molCO2·m−2 was sequestered as GPP. Consequently, the Chenhu wetland functioned as a net carbon sink, with a total of 26.59 molCO2·m−2 (Figure 4b).
During the first half of 2022, the value of R e c o generally exhibited a predominantly upward trajectory, peaking in July at a maximum of 6.84 molCO2·m−2·month−1. Following this peak, a declining trend was observed, with R e c o reaching a minimum of 1.18 molCO2·m−2·month−1 by December. Concurrently, the GPP values increased progressively alongside the growth of vegetation, such as reeds, from the onset of the growing season, achieving a peak of 12.37 molCO2·m−2·month−1 in May, followed by a subsequent decline. The lowest recorded GPP value occurred in January, at 0.03 molCO2·m−2·month−1. In 2022, the Chenhu wetland ecosystem demonstrated CO2 absorption during the growing season, amounting to 26.59 molCO2·m−2, while exhibiting CO2 emissions during the non-growing season, totaling 5.39 molCO2·m−2. The annual NEE, R e c o , and GPP values were recorded at −21.20, 49.61, and 70.81 molCO2·m−2, respectively. The annual NEE value is closely aligned with the average NEE of Asian wetlands, which is −19.68 molCO2·m−2·year−1 [32], suggesting that the Chenhu wetland functions predominantly as a carbon sink. The overall carbon uptake of the ecosystem is influenced by the interplay between photosynthetic assimilation and the efflux of ecosystem respiration [2]. Furthermore, the ratio of R e c o to GPP serves as an indicator of the relative contributions of the carbon exchange processes to the total annual exchange [33]. The annual R e c o /GPP ratio for the Chenhu wetland was calculated to be 0.70, which is lower than the average R e c o /GPP ratio of 0.83 observed in the FLUXNET ecosystem [34]. Consequently, the Chenhu wetland ecosystem is characterized by relatively low ecosystem respiration and a high net uptake of CO2 through plant photosynthesis, resulting in significant CO2 storage within the wetland ecosystem throughout the carbon exchange process.
The carbon exchange dynamics within wetland ecosystems demonstrate considerable spatial heterogeneity, which can be attributed to variations in wetland types, climatic conditions, geographical factors, and management practices [35]. When compared to other subtropical wetlands in China, as depicted in Figure 5 and detailed in Table 1, the annual CO2 absorption of the Chenhu wetland is marginally higher than that of the Chongxi wetland (18.75 molCO2·m−2) [36] and the Beihai wetland (19.48 molCO2·m−2) [31]. However, it is slightly lower than the CO2 absorption rates recorded for Taihu Lake (25.25 molCO2·m−2) [37], Poyang Lake (28.29 molCO2·m−2) [38], and Dongting Lake (33.36 molCO2·m−2) [24]. Furthermore, the CO2 absorption capacity of the Chenhu wetland is significantly less than that of Yunxiao (Zhangjiangkou mangrove wetland) (45.66 molCO2·m−2) [39], Hongze Lake (49.53 molCO2·m−2) [40], the eastern Chongming tidal flat (53.58 molCO2·m−2) [41,42,43], and Gaoqiao (Zhanjiang mangrove wetland) (61.48 molCO2·m−2) [44]. The observed variation in carbon sequestration capacity among subtropical wetland ecosystems can be attributed to differences in meteorological and hydrological conditions across different years, as well as the distinct vegetation compositions present in various wetlands [45]. For example, mangroves serve as the predominant plant species in the flux towers in the Zhangjiangkou and Zhanjiang mangrove wetlands, whereas poplar plantations represent the primary vegetation in the flux tower at Hongze Lake. In contrast, the vegetation surrounding the flux tower at the eastern Chongming tidal flat is predominantly composed of a mixed community of reeds and Spartina alterniflora. The substantial aboveground biomass present in these wetlands significantly contributes to CO2 flux [46].
Research demonstrates that various ecosystems exhibit distinct dynamics regarding carbon sources and sinks, which are influenced by differing vegetation characteristics and environmental conditions. Furthermore, even within the same ecosystem, ecological impacts can vary due to changes in the environment [11]. In 2022, the Northern Hemisphere experienced a significant drought, leading to annual rainfall levels in the Chenhu wetland that were markedly lower than those recorded in previous years. Additionally, both the maximum and minimum temperatures during this period were elevated compared to the same timeframe in the preceding year. To obtain more accurate measurements of carbon flux within the Chenhu wetland ecosystem, it is imperative to implement extended and continuous monitoring efforts.

3.3. Relationships Between NEE and Some Environmental Parameters

The CO2 flux within wetland ecosystems is influenced by a range of environmental parameters. Table 2 and Table 3 illustrate the relationships between the monthly average diurnal variation of NEE and several environmental factors, including T a , T s , PAR, VPD, SWC, and precipitation (P). During daylight hours, NEE exhibited the strongest correlation with PAR, followed by RH, T a , and VPD. Conversely, during nighttime, NEE demonstrated a significant correlation with RH, VPD, and T a .
Path analysis has been widely utilized to evaluate the significance of various environmental factors in relation to seasonal and interannual variations in carbon flux [47]. This analytical method represents an advanced form of multiple regression analysis, enabling the examination of the relationships among different environmental variables and their direct and indirect effects on carbon flux. Based on the aforementioned analytical findings, the path analysis was employed to investigate both the direct and indirect influences of key environmental parameters on NEE within the Chenhu wetland during the growing season (Figure 6). The model exhibited a satisfactory fit, as evidenced by a normed fit index (NFI) and comparative fit index (CFI) exceeding 0.9, along with a p-value greater than 0.05, thereby confirming the model’s robustness. The path coefficients for VPD and PAR were recorded at 1.03 and -0.89, respectively, while RH and Ta displayed coefficients of 0.66 and -0.33, respectively. In general, the diurnal CO2 flux within the wetland was predominantly influenced by VPD and PAR during the growing season, a conclusion that is consistent with the research conducted by Cao et al. [2] in the Qinghai Lake alpine wetland.
Throughout the diurnal cycle, fluctuations in NEE were primarily influenced by leaf assimilation, with PAR serving as the principal determinant of plant photosynthesis [48]. During the growing season of 2022, the capacity for CO2 absorption in the Chenhu wetland during daylight hours exhibited a positive correlation with increasing solar radiation. The relationship between NEE and PAR during daytime adhered to the Michaelis–Menten light response model, which can be mathematically represented by a rectangular hyperbolic equation [27]. This non-linear relationship demonstrates seasonal variations in accordance with vegetation growth. As illustrated in Figure 7 and Table 4, below the light compensation point, NEE values were positive during daylight hours, indicating that the respiratory intensity of the ecosystem exceeded that of photosynthetic production. As PAR increased, both photosynthesis and net carbon absorption also rose. The carbon absorption capacity of the wetland ecosystem reached a saturation point when PAR levels attained light saturation. Furthermore, the ecosystem’s apparent initial radiation use efficiency (α), maximum photosynthetic capacity ( P m a x ), and daytime ecosystem respiration rate ( R e c o , d a y ) within the Chenhu wetland exhibited notable seasonal fluctuations. The variable α reached its peak value in April, followed by a gradual decline. In contrast, P m a x attained its maximum in August, with subsequent peaks in May and July, and recorded its lowest value in September. R e c o , d a y   was observed to be at its highest in both April and May, followed by July and August, while it reached its minimum in September. The mean value of α during the growing season was determined to be 0.0398 μmolCO2·μmol−1, which is lower than the values reported for reed wetlands such as the Yellow River Delta (0.0545 μmolCO2·μmol−1) [49], Panjin wetland (0.065 μmolCO2·μmol−1) [5,50], and Dajiuhu peatland (0.0559 μmolCO2·μmol−1) [19]. The average value of P m a x was calculated to be 17.30 μmolCO2·m−2·s−1, which is comparable to that of the eastern Chongming tidal flat wetland (15.20 μmolCO2·m−2·s−1) [43] and exceeds that of the Dajiuhu peatland (11.85 μmolCO2·m−2·s−1) [19]. This may be attributed to the fact that the average daily PAR values during the growing season in the Chenhu wetland (ranging from 355.62 to 2215.35 μmol·m−2·s−1) were greater than those observed in the Dajiuhu peatland (which ranged from 103.32 to 1253.23 μmol·m−2·s−1) [19]. The fluctuations in the values of P m a x and α are associated with vegetation growth and environmental conditions. In 2022, the Chenhu wetland experienced lower precipitation levels compared to previous years, particularly after April. Drought conditions are known to adversely affect the photosynthetic parameters ( P m a x and α) of plants. Furthermore, it has been observed that the photosynthetic parameters ( P m a x and α) tend to decline towards the end of the growing season, correlating with reductions in leaf area index and temperature [51]. This trend aligns with the variations in photosynthetic parameters observed in other ecosystems [52].
The response process of CO2 flux to light in the Chenhu wetland during the growing season, under varying VPD conditions, is illustrated in Figure 8. The results indicate that PAR and daytime NEE demonstrate distinct response patterns contingent upon the levels of VPD. Specifically, when VPD is below 0.5 kPa, PAR levels are low, which hampers the ability of plant leaves to reach light saturation during photosynthesis. A strong linear correlation between NEE and PAR was observed during daylight hours, aligning with the findings of previous studies conducted by Lilu Wu [48] and Lingling Xu [53]. Conversely, when VPD exceeds 0.5 kPa, NEE during daylight hours progressively transitions to negative values alongside an increase in PAR. This transition signifies an enhancement in carbon dioxide absorption by the wetland ecosystem. A significant rectangular hyperbolic relationship was observed between these two variables, as depicted in Figure 8. Additionally, both the P m a x and α were found to be elevated when VPD ranged from 0.5 to 1.5 kPa, in contrast to conditions where VPD surpassed 1.5 kPa. When VPD exceeds 1.5 kPa, an increase in PAR correlates with an enhancement in daytime carbon dioxide absorption. However, the elevated VPD induces partial stomatal closure in the leaves of wetland vegetation, which subsequently results in a decrease in α and hinders the overall photosynthetic process in these plants [54]. Since September 2022, the Chenhu wetland has been subjected to a prolonged drought. This extended period of low precipitation, combined with elevated VPD, has led to a significant inhibition of photosynthesis, resulting in the lowest recorded values of P m a x , α, and R e c o , d a y . An analysis of the response characteristics of daytime NEE to PAR across varying VPD levels in the Chenhu wetland indicates that the optimal VPD range for plant photosynthesis in this region is between 0.5 and 1.5 kPa. Within this range, the correlation between daytime NEE and PAR is significantly enhanced, and both P m a x and α exhibit elevated values.
Temperature serves as the primary environmental determinant influencing ecosystem respiration. The relationship between Ta and CO2 flux during both the growing and non-growing seasons is depicted in Figure 9. It is evident that CO2 flux exhibits an exponential increase in response to rising temperatures. However, the data points presented in Figure 9 demonstrate considerable variability due to the influence of additional environmental factors. During the non-growing season, Ta accounts for 26.56% of the variability in CO2 flux, indicating its significant role as a controlling factor during this period. Conversely, in the growing season, Ta explains only 7.61% of the variability in nighttime CO2 flux, suggesting that other environmental factors exert a more pronounced influence during this time. Furthermore, temperature primarily impacts plant photosynthesis by modulating the activity of leaf enzymes [55]. During the non-growing season, lower temperatures were observed, and a strong correlation was established between CO2 flux and Ta. This suggests that at reduced temperatures, an increase in Ta can enhance the enzymatic activity of plant leaves, resulting in a greater rate of plant respiration compared to photosynthesis, thereby leading to an increase in CO2 emissions. The sensitivity coefficient (Q10) for Ta during the night in both the growing and non-growing seasons was recorded at 1.27 and 1.75, respectively. These values are consistent with findings from previous studies conducted on wetland ecosystems, such as the eastern Chongming tidal flat wetland [43] and the Yellow River Delta [48], indicating that the NEE of the Chenhu wetland ecosystem exhibits significant responsiveness to fluctuations in temperature.

4. Conclusions

The diurnal variation of CO2 flux during the growing season of the Chenhu wetland ecosystem in 2022 exhibited considerable significance, characterized by substantial differences in the daily fluctuations of NEE throughout the month. The predominant trend was represented by a U-shaped curve, indicating CO2 absorption during daylight hours followed by its release at night. Throughout the growing season, the wetland functioned as a carbon sink, whereas it transitioned to a carbon source during the non-growing season. The recorded range of NEE variation during the growing season spanned from −12.42 ± 8.00 to 4.28 ± 5.45 μmolCO2·m−2·s−1, with June demonstrating the longest average duration of carbon sink, lasting 11.5 h. In contrast, the duration of carbon sink was significantly shorter in January–February and December, during which the ecosystem predominantly exhibited carbon emissions throughout the day. The annual NEE, R e c o , and GPP of the Chenhu wetland ecosystem were recorded at −21.20, 49.61, and 70.81 molCO2·m−2, respectively. Given the significant drought experienced in the Northern Hemisphere in 2022, it is imperative to implement more extensive monitoring efforts to obtain more precise measurements of CO2 flux within the Chenhu wetland.
During the growing season, daytime NEE was primarily influenced by PAR, consistent with the principles of the Michaelis–Menten light response model. VPD has been identified as a limiting factor affecting daytime net carbon exchange. An increase in VPD was found to reduce the sensitivity of daytime NEE to PAR. The optimal range for VPD was determined to be between 0.5 kPa and 1.5 kPa. In the non-growing season, the principal factors influencing net carbon exchange include RH, VPD, and T a . The relationship between T a   and NEE aligns with the principles established in the van’t Hoff model. These findings enhance the understanding of CO2 exchange dynamics within the lake-marsh wetland ecosystem in the middle reaches of the Yangtze River and contribute to the prediction of future responses of wetland ecosystems to changes in environmental factors.

Author Contributions

Conceptualization, Y.Z. and L.L.; methodology, W.W.; formal analysis, H.L. and P.L.; resources, P.L., L.L. and Y.Z.; writing—original draft preparation, Y.Z. and L.L.; writing—review and editing, Y.Z. and W.W.; project administration, H.L. and P.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Minsheng Geological Project of Hubei Geological Bureau (MSDZ202316, MSDZ202404).

Data Availability Statement

Data are available from the authors by request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Sketch map of the study area.
Figure 1. Sketch map of the study area.
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Figure 2. The monthly variation of environmental factors in the Chenhu wetland during the year 2022. (a) Air temperature ( T a ), soil temperature ( T s ), and average T a in Wuhan from 2011 to 2021. (b) Wind speed (WS), photosynthetically active radiation (PAR). (c) Relative humidity (RH), vapor pressure deficit (VPD). (d) Soil water content (SWC), total precipitation (PPT), and average PPT in Wuhan from 2011 to 2021.
Figure 2. The monthly variation of environmental factors in the Chenhu wetland during the year 2022. (a) Air temperature ( T a ), soil temperature ( T s ), and average T a in Wuhan from 2011 to 2021. (b) Wind speed (WS), photosynthetically active radiation (PAR). (c) Relative humidity (RH), vapor pressure deficit (VPD). (d) Soil water content (SWC), total precipitation (PPT), and average PPT in Wuhan from 2011 to 2021.
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Figure 3. Diurnal variation of NEE in the Chenhu Wetland during 2022 ((a) growing season; (b) non-growing season).
Figure 3. Diurnal variation of NEE in the Chenhu Wetland during 2022 ((a) growing season; (b) non-growing season).
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Figure 4. The daily variation of NEE on an annual basis (a) and the seasonal variation of monthly total NEE (b) for the Chenhu wetland during the year 2022.
Figure 4. The daily variation of NEE on an annual basis (a) and the seasonal variation of monthly total NEE (b) for the Chenhu wetland during the year 2022.
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Figure 5. Comparison of annual NEE in subtropical wetlands.
Figure 5. Comparison of annual NEE in subtropical wetlands.
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Figure 6. Path analysis between main environmental factors and NEE. χ2 = 0.5, p = 0.46 > 0.05, Degrees of freedom = 1. NFI = 0.999, CFI = 1.000, root mean square error of approximation (RMSEA) = 0.
Figure 6. Path analysis between main environmental factors and NEE. χ2 = 0.5, p = 0.46 > 0.05, Degrees of freedom = 1. NFI = 0.999, CFI = 1.000, root mean square error of approximation (RMSEA) = 0.
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Figure 7. Scatter plots of daytime NEE against PAR from April to September 2022. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.
Figure 7. Scatter plots of daytime NEE against PAR from April to September 2022. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.
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Figure 8. Scatter plots of daytime NEE against PAR under different VPD during the growth season. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.
Figure 8. Scatter plots of daytime NEE against PAR under different VPD during the growth season. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.
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Figure 9. Scatter plots of NEE against Ta of Chenhu wetland during the night in the growing season (a) and throughout the day in the non-growing season (b). The fitting results and correlation coefficients between NEE and Ta are also shown.
Figure 9. Scatter plots of NEE against Ta of Chenhu wetland during the night in the growing season (a) and throughout the day in the non-growing season (b). The fitting results and correlation coefficients between NEE and Ta are also shown.
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Table 1. Comparison of annual NEE (unit molCO2·m−2) between Chenhu wetland and other subtropical wetlands in China.
Table 1. Comparison of annual NEE (unit molCO2·m−2) between Chenhu wetland and other subtropical wetlands in China.
Study SiteLongitude/LatitudeWetland TypeDominant Plant SpeciesMeasuring YearAnnual NEEReferences
Yunxiao wetland23.9240° N, 117.4147° EEstuarine mangrove wetlandKandelia obovata, Avicennia marina, Aegiceras corniculatum2019~202045.66[39]
Dongting Lake 29.4875° N, 113.0525° ELake wetlandMiscanthus sacchariflorus2014~201633.36[24]
Eastern Chongming tidal flat31.6333° N, 121.9667° ECoastal wetlandPhragmites australis, Spartina alterniflora, Scirpus mariqueter2005~2007, 201253.58[41,42,43]
Gaoqiao21.57° N, 109.76° ECoastal wetlandAegiceras corniculatum, Bruguiera gymnorrhiza201061.48[44]
Chongxi wetland31.72° N, 121.23° EEstuarine wetlandPhragmites australis200918.75[36]
Poyang Lake28.2° N~30.0° N, 115.5° E~116.5° ELake wetlandCarexcinerascens, Phalarisarundinacea, Miscanthussacchariflorus, Phragmites communis2013.08~2016.0628.29[38]
Taihu wetland30.9278° N~5494° N, 119.8756° E~119.6028° ELake wetlandPotamogeton malaianus, Hydrilla verticillata2012~201725.25[37]
Hongze Lake33.55° N, 118.53° ELake wetlandPopulus× euramericana cv. Nanlin-95, Bidens tripartita L., Setaria viridis (L.) Beauv.2017.5~2018.1249.53[40]
Beihai wetland25.1218° N, 98.5561° EAlpine marshCyperus duclouxii E.-G. Camus, Oberonia iridifolia Roxb. ex Lindl2015.06~2016.1219.48[31]
Chenhu wetland30.3270° N, 113.8617° ELake-marsh wetlandPhragmites conmunis, Zizaniacaduaflora Ass202221.20This study
Table 2. Pearson correlation coefficients of the monthly mean daily variation of NEE and environmental parameters during daytime.
Table 2. Pearson correlation coefficients of the monthly mean daily variation of NEE and environmental parameters during daytime.
MonthTaPARTsRHVPDSWCP
1−0.0080.469 *−0.1560.051−0.0540.143−0.120
2−0.143−0.643 **0.3830.139−0.1110.130−0.044
3−0.279−0.903 **0.837 **0.342−0.2670.2120.273
4−0.355−0.947 **0.858 **0.341−0.3290.592 **0.051
5−0.463 *−0.972 **−0.1750.513 **−0.432 *0.435 *0.168
6−0.500 **−0.939 **0.0700.575 **−0.521 **−0.1120.404 *
7−0.529 **−0.949 **−0.2970.657 **−0.611 **0.0880.239
8−0.519 **−0.926 **−0.527 **0.640 **−0.604 **0.0860.115
9−0.501 **−0.914 **0.1340.548 **−0.492 *0.1790.474 *
10−0.571 **−0.930 **0.1860.603 **−0.550 **0.1550.256
11−0.560 **−0.913 **0.1910.549 **−0.541 **0.101−0.103
12−0.478 *−0.453 *−0.514 *0.573 **−0.529 *−0.564 **−0.417
Note: * and ** are significantly correlated at 0.05 (bilateral) and 0.01 (bilateral) levels, respectively.
Table 3. Pearson correlation coefficients of the monthly mean daily variation of NEE and environmental parameters at night.
Table 3. Pearson correlation coefficients of the monthly mean daily variation of NEE and environmental parameters at night.
MonthTaTsRHVPDSWCP
1−0.222 0.285 0.229 −0.235 −0.148 0.317
20.127 −0.170 −0.183 0.177 0.191 0.066
3−0.143 0.838 ** 0.150 −0.207 0.570 ** 0.081
4−0.352 −0.389 0.322 −0.324 −0.339 −0.131
5−0.773 ** −0.650 ** 0.743 ** −0.756 ** −0.020 −0.201
6−0.413 −0.414 0.504 * −0.511 * 0.156 0.094
7−0.715 ** −0.466 * 0.702 ** −0.711 ** 0.526 * 0.389
8−0.611 ** −0.493 ** 0.614 ** −0.625 ** 0.625 ** −0.520 *
9−0.001 −0.054 0.026 −0.036 −0.049 -
10−0.594 ** 0.175 0.601 ** −0.590 ** 0.241 0.336
11−0.548 ** 0.110 0.501 ** −0.543 ** 0.256 0.059
12−0.172 −0.362 0.168 −0.178 −0.004 0.016
Note: * and ** are significantly correlated at 0.05 (bilateral) and 0.01 (bilateral) levels, respectively.
Table 4. Simulated parameters of light response curve in the growth season.
Table 4. Simulated parameters of light response curve in the growth season.
Monthα
(μmolCO2·μmol−1)		 P m a x (μmolCO2·m−2·s−1) R e c o , d a y (μmolCO2·m−2·s−1)Number of Samples (n)R2p
April0.075714.733.807730.25<0.01
May0.054421.513.468630.47<0.01
June0.047916.222.778650.51<0.01
July0.028619.313.388950.65<0.01
August0.013621.923.108370.58<0.01
September0.018610.102.057610.36<0.01
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Zhang, Y.; Liu, L.; Luo, H.; Wang, W.; Li, P. Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China. Water 2024, 16, 3169. https://doi.org/10.3390/w16223169

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Zhang Y, Liu L, Luo H, Wang W, Li P. Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China. Water. 2024; 16(22):3169. https://doi.org/10.3390/w16223169

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Zhang, Ya, Li Liu, Hua Luo, Wei Wang, and Peng Li. 2024. "Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China" Water 16, no. 22: 3169. https://doi.org/10.3390/w16223169

APA Style

Zhang, Y., Liu, L., Luo, H., Wang, W., & Li, P. (2024). Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China. Water, 16(22), 3169. https://doi.org/10.3390/w16223169

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