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Article

Variations in δ13CDIC and Influencing Factors in a Shallow Macrophytic Lake on the Qinghai–Tibetan Plateau: Implications for the Regional Carbon Cycle and Sustainable Development

by
Yanxiang Jin
1,2,3 and
Xin Jin
1,2,3,*
1
College of Geographical Science, Qinghai Normal University, Xining 810016, China
2
Key Laboratory of Physical Geography and Environmental Processes, Xining 810016, China
3
Academy of Plateau Science and Sustainability, Xining 810016, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(8), 3350; https://doi.org/10.3390/su16083350
Submission received: 16 November 2023 / Revised: 5 April 2024 / Accepted: 8 April 2024 / Published: 17 April 2024
(This article belongs to the Special Issue Plateau Ecosystem Carbon Cycling: Towards Sustainable Ecosystem Management)
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Abstract

:
Lake carbon cycle in lake ecosystems is critical for regional carbon management. The application of carbon isotope techniques to terrestrial and aquatic ecosystems can accurately elucidate carbon flow and carbon cycling. Lake ecosystems on the Qinghai–Tibetan Plateau are fragile and sensitive to climate and environment changes, and the carbon cycle impact on the carbon isotopic composition (δ13C) of dissolved inorganic carbon (DIC) in these systems has not been well studied, limiting the ability to devise effective management strategies. This study explored the relationship among the δ13C position of the DIC (δ13CDIC) in Genggahai Lake, the lake environment, and the climate of the watershed based on the observed physicochemical parameters of water in areas with different types of submerged macrophyte communities, combined with concomitant temperature and precipitation changes. Overall, the Genggahai Basin δ13CDIC exhibited a large value range; the average δ13CDIC for inflowing spring water was the most negative, followed by the Shazhuyu River, and then lake water. Variations in the photosynthetic intensity of different aquatic plants yielded significantly changing δ13CDIC-L values in areas with varied aquatic plant communities. Hydrochemical observations revealed that δ13CDIC-I and aquatic plant photosynthesis primarily affected the differences in the δ13CDIC-L values of Genggahai Lake, thereby identifying them as the key carbon cycle components in the lake. This improves the understanding of the carbon cycle mechanism of the Qinghai–Tibetan Plateau Lake ecosystem, which is beneficial to improving sustainable lake development strategies.

1. Introduction

Accounting for approximately 3.7% of the global land area, lakes are an important component of inland water systems that regulate the carbon cycle by storing, transporting, and transforming carbon [1,2,3,4]. Information on the lake carbon cycle can contribute to a comprehensive understanding of the terrestrial carbon cycle [5,6], which is anticipated to play an important role in China’s 2060 carbon neutral commitment [7]. Understanding the lake carbon cycle is of great significance to research regional and global carbon budget, which is a foundational work to establish the regional carbon management and sustainable development policy.
Dissolved inorganic carbon (DIC) occurs in water as CO2, CO32−, HCO3−, and H2CO3. Carbon isotopes (δ13C) record the cycling of carbon during each of these links, which involves equilibrium and kinetic fractionation [8,9]. Thus, δ13C analyses of DIC (δ13CDIC) provide a powerful tool for tracing the carbon cycle in lakes and elucidating carbon fluxes [10,11].
Lake ecosystems on the Qinghai–Tibetan Plateau are fragile and sensitive to the changes in the climate and environment [12]. Lakes on the Qinghai–Tibetan Plateau account for approximately 57% of the total lake area in China [13]. Understanding the carbon cycling process of lakes on the Qinghai–Tibetan Plateau is of profound significance for regional ecological environment protection and sustainable development. However, most studies on the δ13CDIC in lakes on the Tibetan Plateau have focused on the climatic and environmental implications of the δ13C of lake sediments and have not considered the regional carbon cycle. Furthermore, variations in the δ13CDIC of various lake types may have different responses to carbon cycle processes. For example, Lei et al. [10] analysed the characteristics of the δ13CDIC of 24 lakes (mainly closed lakes) across the Qiangtang Plateau, revealing that the high δ13CDIC values of closed lakes could be primarily attributed to significant catchment-scale contributions from carbonate weathering and the evasion of dissolved CO2 induced by enhanced lake water evaporation. Therefore, performing a comprehensive study on the changes in the δ13CDIC values of lakes on the Qinghai–Tibetan Plateau is necessary.
Genggahai (GGH) Lake is a shallow macropytic lake on the Qinghai–Tibetan Plateau. Due to less influence by human activity and aquatic plants grow flourishingly, variations in δ13CDIC of the GGH Lake is related to carbon cycle in the basin, which is affected by regional climate and environment change and biological activity. This study explored the relationships among the δ13CDIC values in the GGH Lake, the lake environment, and the watershed climate based on the observed water physicochemical parameters in areas with different types of submerged macrophyte communities, as well as the changes in the temperature and precipitation during the same period. Our objective was to identify the influencing factors of δ13CDIC in the GGH Lake and reveal its implications for the carbon cycle in an closed lake system.

2. Study Area

Gonghe Basin (35.45°–36.93° N, 98.77°–101.37° E) is located in the north-eastern Tibetan Plateau (Figure 1a) at a mean elevation of approximately 3000 m. This region is characterised by an alpine arid and semi-arid continental climate [14] (pp. 1–166). From the meteorological data from Gonghe Station from 2000 to 2020, the mean annual temperature was ~5.14 °C and the mean annual precipitation was ~325 mm; the annual potential evaporation precipitation was ~1183 mm (http://data.cma.cn/, 1 November 2021). The basin has an elongated shape, which is surrounded by the Xiqing, Heka, Ela, Wahong, Waligong, and South Qinghai mountains (Figure 1a) [14] (pp. 1–166).
The GGH Lake (36.18° N, 100.1° E) is located in the central Gonghe Basin (Figure 1a). The present-day area of the lake is approximately 2.0 km2, with a surface elevation of 2860 m, maximum depth of 1.8 m, water salinity of 1 g L−1, and pH of 9.2 ± 0.5 [16]. The lithology of the GGH Basin is dominated by limestone, sandstone, marble, slate, and schist [14] (pp. 1–166). The lake has no natural discharge outlets or direct surface inflows and is fed mainly by groundwater. Within the lake basin, springs emerge as artesian water. Small spring-water streams emanate from sediment outcrops in the northwest part of the catchment and feed the GGH Lake and sustain the grassland [17] (Figure 1b). The lake is inhabited by numerous submerged macrophytes, such as Chara spp., Myriophyllum spicatum L., and Potamogeton pectinatus L. (Figure 1b), as well as gastropod molluscs [16]. The entire lake is surrounded by grassland, particularly the desert grassland ecosystem dominant in the GGH Basin. This region is characterised by strong wind-blown sand movement. Human activity remains limited, with only a small number of Tibetan herdsmen, who graze their livestock on the grasslands surrounding the lake.

3. Materials and Methods

Between 2012 and 2015, from May to September of each year, lake water samples were collected at a depth of 20 cm in three different areas to characterise the Chara spp., M. spicatum, and P. pectinatus communities. Meanwhile, watershed groundwater samples and surface water samples from the Shazhuyu River were also collected multiple times. Due to the freezing of lake water, water samples were not collected from October to the following April. The collected samples were stored in 500 mL polyethylene plastic bottles; after adding 1 mL of HgCl2 solution, each bottle was sealed with a sealing membrane. A total of 53 lake water samples, 19 groundwater samples, and 17 river water samples were collected. The physical and chemical parameters (i.e., the temperature, dissolved oxygen (DO), and pH) (Table 1) were measured in situ using a portable water quality analyser (AquaRead-1000, Aquaread, Broadstairs, UK).
The plant samples, including Chara spp., M. spicatum, and P. pectinatus, were sampled monthly adjacent to the lake water sampling points. Additionally, Gyraulus sibiricus samples adhered to Chara spp. were collected. The fresh plant samples and shells were stored in polyethylene plastic bags and numbered before being transported to the lab for further pre-treatment and analysis.
The water samples were subjected to the following laboratory pre-treatment procedure before DIC isotope determination. First, each sample was filtered through a 0.45 μm glass-fibre filter, and 4 mL of a saturated BaCl2 solution was added to induce BaCO3 precipitation [18]. After the samples settled, the clear liquid was siphoned away; the remaining solids were placed in a drying oven to dry at a constant low temperature of 30 °C. Finally, the dried samples were ground to a uniform size, and appropriate amounts were weighed for the DIC isotope determination. The prepared DIC samples were tested using a MAT-253 isotope ratio mass spectrometer (Thermo-Finnigan, Bremen, Germany), together with a micro-carbonate sampling device (Kiel IV). Tests were performed at the Key Laboratory of Western China’s Environmental Systems, MOE, Lanzhou University. Test results were reported relative to the PDB standard. Analytical precision values for δ13C and δ18O were ±0.03‰ and ±0.05‰, respectively.
To analyse the δ13C of the plant bulk organic matter (δ13Corg), samples were pre-treated based on the methods reported by Song et al. [19]. The δ13Corg was analysed using an on-line Conflo III-DeltaPlus isotope ratio mass spectrometry, combined with a Flash EA1112 elemental analyser at Lanzhou University (Lanzhou, China). The results were reported in ‰ relative to PDB. The standard samples were glycine, puge, and wheat, whose standard deviations were 0.04, 0.07, and 0.05, respectively. All values are reported and discussed in the following section. Temperature data from the Gonghe Station were obtained from the China Meteorological Data Service Centre: Daily data from surface meteorological stations in China.
The Grapher 8 and Coreldraw X8 software were used for drawings. Pearson correlation analysis was applied by SPSS 17.0 statistical software. Pearson correlation analysis is a statistical method used to evaluate the strength and direction of a linear relationship between two variables. Based on covariance, it calculates a correlation coefficient between −1 and 1. The results of Pearson correlation analysis include two values: correlation coefficient and p-value. The correlation coefficient shows the direction and strength of the linear relationship between two variables. A value close to 1 indicates a high positive correlation between the two variables and a value close to −1 indicates a high negative correlation, and a value close to 0 indicates that the two variables are independent of each other and have no correlation. The p-value represents an indicator of whether two variables are significantly correlated. If the p-value is less than 0.05, the two variables are considered to be significantly correlated.

4. Results

4.1. Physical and Chemical Parameters of Lake Water

The overall temperature and pH trends of the lake water were relatively consistent in all three plant communities with the previously published physical and chemical parameters of water from the GGH Lake measured from 2012 to 2013 [20] (Figure 2a,b). Specifically, the overall temperature remained relatively constant throughout the entire monitoring period, but its annual fluctuations were more notable, with higher temperatures in June, July, and August, and relatively low temperatures in May and September (Figure 2a). From 2012 to 2015, the lake water pH exhibited an overall gradually increasing trend from May to September, with only a few exceptions (e.g., June and July 2015; Figure 2b). Beginning in May, both the number of aquatic plants and water temperature of the lake increased, which led to the increased consumption of CO2 and HCO3− via plant photosynthesis. Moreover, the variation in DO was not notable, with all three plant communities showing inconsistencies, which may have been related to the high sensitivity of DO to temperature changes (Figure 2c).
A comparison of the three plant communities showed that, except for 2012, the pH values in the Chara spp. community were slightly higher than those in the P. pectinatus and M. spicatum communities throughout the testing period (Figure 2a,b). Differences in the DO values of lake water in the three communities were more prominent; the Chara spp. community had relatively higher DO values in June and July (Figure 2c). Compared to vascular plants (P. pectinatus and M. spicatum communities), Chara spp. community has higher photosynthesis rates and lower respiration rates [21].

4.2. Spatial Variations in DIC Isotopic Composition in the GGH Basin Waterbodies

From 2012 to 2015, the δ13CDIC values of water from the GGH Lake (δ13CDIC-L) ranged from −17.3 to 1.6‰, with a mean value of −6.91‰. The δ13CDIC of water from the Shazhuyu River (δ13CDIC-R) ranged from −15.9 to −5.6‰, with a mean value of −10.8‰. The δ13CDIC for the groundwater spring, a lake water source, ranged from −17.3 to −1.1‰, with a mean value of −11.1‰. Overall, the δ13CDIC-L values were the most positive, followed by the δ13CDIC-R values, whereas the δ13CDIC values of the inflowing spring water (δ13CDIC-I) were the most negative (Figure 3).
The δ13CDIC-L values in the Chara spp. community were more positive than those in the M. spicatum and P. pectinatus communities. From 2012 to 2015, the δ13CDIC-L values in the Chara spp. community ranged from −9.9 to 1.6‰, with a mean value of −5.4‰. In the P. pectinatus community, the δ13CDIC-L values ranged from −15.2 to −3.9‰, with a mean value of −7.4‰. In the M. spicatum community, the δ13CDIC-L values ranged from −17.3 to −2.9‰, with a mean of −7.9‰. Except for one month (September 2012), the P. pectinatus and M. spicatum communities had similar δ13CDIC-L values and both communities had more negative values than the Chara spp. community (Figure 3a).

4.3. Temporal Variations in DIC Isotopic Compositions in the GGH Basin Waterbodies

In the Chara spp. community, the δ13CDIC-L values were relatively positive in July from 2012 to 2014, while, in 2015, there was a gradual decrease. In contrast, the δ13CDIC-L values of the P. pectinatus and M. spicatum communities did not show a seasonal bias of more positive values (Figure 3a). Figure 4a shows the interannual variations in the δ13CDIC-L values. From 2012 to 2015, the mean δ13CDIC-L values of the Chara spp. community were all more positive (−4.0‰), whereas in 2013 and 2014, these values were more negative (−6.8‰). From 2012 to 2015, the δ13CDIC-L values in the P. pectinatus and M. spicatum communities showed a gradual positive trend. In comparison, the δ13CDIC-I values from 2012 to 2015 showed an overall increase; however, in 2013 and 2014, these values became increasingly negative (Figure 4b). During the same period, the δ13CDIC-R values gradually increased (Figure 4b).

5. Discussion

5.1. Factors Affecting δ13CDIC in the GGH Basin Waterbodies

Several major processes affect the stable carbon isotope compositions of lake water DIC, such as in-lake processes (including lake metabolism, organic matter decomposition, calcite precipitation, and exchange with atmospheric CO2) and the climatic and geographical environment of the catchment (including carbonate rock weathering, dissolution, and soil respiration) [22]. The climatic and geographical environment in the catchment can alter the carbon isotope composition of the lake water DIC by influencing the aqueous CO2 and alkalinity of inflowing water. Generally, the five following factors affect the isotope composition of the DIC.
(1)
Lake inflow carbon isotope composition. The isotopic composition of the lake inflow directly affects the isotopic composition of the lake. This is especially significant in exorheic lakes or lakes with a short water retention time. In the Chara spp. community and P. pectinatus growth area, the δ13CDIC-L and δ13CDIC-I values were positively correlated, whereas in the M. spicatum community growth area, these values showed no correlation (Figure 5). This is because the M. spicatum community was located far from the small spring-water streams, where weak exchange with spring water took place (Figure 1b). This reveals that the δ13CDIC-I values were key factors influencing the δ13CDIC-L values.
(2)
Exchange with atmospheric CO2. The DIC pool in the lake tends to be in isotopic equilibrium with the atmosphere via CO2 exchange. During this process, 12C-rich CO2 is preferentially released from the lake surface into the atmosphere, resulting in a DIC pool that is enriched in 13C. This exchange process is slow; therefore, its effect on the δ13CDIC-L values is more notable in endorheic lakes with a long retention time. However, it is not easily observable in lakes with short retention times or rapid circulation [23]. When the exchange between lake water and atmospheric CO2 reaches an equilibrium, the δ13CDIC-L values range from 1 to 3‰ [24,25].
The exchange between the DIC of lake water and atmospheric CO2 is continuous in exorheic lakes. At equilibrium, isotope fractionation occurs between atmospheric CO2 and dissolved carbonate species, i.e., CO2 (aq), HCO3−, and CO32− [8], as follows:
εaq g = −(0.0049 ± 0.003) × T (°C) − (1.31 ± 0.06),
εHCO3 g = −(0.141 ± 0.003) × T (°C) + (10.78 ± 0.05), and
εCO3 g = −(0.052 ± 0.03) × T (°C) + (7.22 ± 0.46)
Temperatures recorded using a water level data logger showed that from May to September of 2013–2015, the mean water surface temperature of the GGH Lake was 17.0 °C. Based on Equations (1)–(3), at the mean water surface temperature in the GGH Lake, the carbon isotope fractionation factors between H2CO3, HCO3−, and CO32− and atmospheric CO2 are −1.5, 8.38, and 5.89‰, respectively. The isotopic composition of global atmospheric CO2 is approximately −8.1‰ [26]. Therefore, at equilibrium, the isotopic values of H2CO3, HCO3−, and CO32− in the lake water are −9.6, 0.28, and −2.21‰, respectively. Different forms of DIC have varying δ13C values at isotopic equilibrium; the magnitude of the δ13CDIC value depends on the proportions of the different DIC forms in lake water, which is related to its pH value [27] (p. 583). When the pH value is 5.5, 80% of the DIC in a water body has an aqueous CO2 form (aq). When the pH is 8.5, CO2 (aq) accounts for <1% of the DIC, which predominantly takes HCO3− and CO32− forms. When the pH reaches 10, HCO3− accounts for <50% of the DIC; however, CO32− dominates the DIC [27] (p. 583). The pH value of the GGH Lake varied from 8.1 to 10.6, indicating that HCO3− and CO32− were the dominant forms of DIC. Notably, the actual δ13CDIC values of the GGH Lake were generally more negative than the atmosphere-equilibrated δ13CDIC.
(3)
Organic matter decomposition in lake sediments. Sedimentary organic matter in lakes includes native aquatic plants and terrestrial organic debris transported into the lake from the surrounding watershed. Once degraded, this organic matter increases the 12C-enriched DIC composition of lake water [9]. Organic matter decomposition in Qingmuke Lake (a freshwater lake located on the Qiangtang Plateau) resulted in a DIC isotope value equal to, or even lower than, that of river water [10]. In contrast, the δ13CDIC-L values in the GGH Lake were significantly more positive than that of the Shazhuyu River, indicating that organic matter decomposition may have had a relatively small effect on the DIC lake composition. Additionally, methane produced by organic matter decomposition resulted in a more negative δ13CDIC value. Organic matter decomposition can cause a decrease in the δ13CDIC values to −50‰ [28]. This value is significantly lower than the mean δ13CDIC-L value of the GGH Lake, which indicates that the CO2 or methane produced via decomposition did not have a significant effect on seasonal or interannual changes in the δ13CDIC-L values.
During the observation period, the mean carbon isotopic composition of organic matter (δ13Corg) in the Chara spp. community was −16.0‰, whereas the mean values of δ13Corg in the P. pectinatus and M. spicatum communities were −12.7 and −11.4‰, respectively. If we neglect the carbon isotope fractionation effect owing to organic matter decomposition, the δ13C of the CO2 released via organic matter decomposition is then equal to δ13Corg. According to Equation (2), if HCO3− is the dominant form of DIC in the lake, then the equilibrium isotopic value of HCO3− in the lake water is 0.28‰. In this case, the mean δ13CDIC-L value of the Chara spp., P. pectinatus, and M. spicatum communities would be −15.72, −12.42, and −11.12‰, respectively. However, the observed mean δ13CDIC-L values for these three communities were −5.4, −7.4, and −7.9‰, confirming that organic matter decomposition has a limited effect on the δ13CDIC-L of the GGH Lake.
(4)
Lake photosynthetic activity. In highly productive lakes, photosynthesis is a key factor that affects the δ13CDIC lake water values [29] (pp. 197–207), [30] (pp. 99–118). During photosynthesis, plants preferentially uptake 12C, which yields more negative δ13C values for plants and the δ13CDIC of the water body becomes more positive [31]. Charaphytes are an important submerged aquatic macrophyte. Compared with vascular plants, charaphytes have a higher photosynthetic rate and lower respiration rate. The preferential uptake of 12CO2 for photosynthetic purposes could have led to the 13C-enrichment of DIC in the lake water [32]. During intense photosynthesis, dissolved CO2 in lake water is limited [33]. When this occurs, charaphytes use HCO3 for photosynthetic activity. Compared with vascular plants, charaphytes can use HCO3− for photosynthetic activity more effectively [21]. According to Equations (1)–(3), the δ13C values of HCO3− were more positive than those of H2CO3 and CO32− in the lake water. In contrast, the photosynthetic activity of charaphytes results in carbonate precipitation in the surrounding waters, forming thick CaCO3 encrustations [33]. This also leads to 13C-enriched water in the charaphyte growth area.
We found that the seasonal bias in the δ13CDIC-L values of the Chara spp. community were more positive in July (2012–2014) (Figure 4a). Additionally, at the beginning of Chara spp. growth (in May), the δ13CDIC-L value of the Chara spp. community was equal to the value at the end of Chara spp. growth (in September), especially in 2012. In contrast, the δ13CDIC-L values in the P. pectinatus and M. spicatum communities showed no seasonality (Figure 4a). This phenomenon may have occurred because Chara spp. has a higher photosynthetic rate and a lower respiration rate than that of the other communities [21]. Pentecost et al. [34] also observed seasonal variations in the δ13CDIC-L values of the Chara spp. community in the UK. Pełechaty et al. [33] suggested that an increase in δ13CDIC results from the intense photosynthetic activity of Chara rudis during the early summer. Moreover, we found that the differences in the δ13CDIC-L values between Chara spp. and vascular plants were smaller at the beginning and end of the growing season in the GGH Lake but larger during the mid-growth season (July) (Figure 4a). There may have been a limited impact from photosynthesis on the δ13CDIC-L values in areas with submerged vascular plants. This trend was not evident during certain months, e.g., in July 2015. Due to data limitations, we cannot provide an explanation for this phenomenon. Nevertheless, we can reasonably conclude that the variations in the δ13CDIC-L values of the lake water were related to the intensity of photosynthetic activity in different aquatic plants.
(5)
Water retention time. In arid regions, with extended lake water residence times, strong evaporation leads to the preferential loss of the light 12CO2 and 16O2 isotopes, yielding more positive δ13CDIC-L and oxygen (δ18O) isotopic lake water compositions; furthermore, a significant positive correlation was observed between δ13CDIC-L and δ18OL [35]. Monitoring results revealed that the δ18OL values of the GGH Lake significantly deviated from the global meteoric water line but were consistent with the local evaporation line, indicating that evaporation affected the δ18OL lake water composition [19,36]. However, this study found that the δ13CDIC-L and δ18OL values of the GGH Lake were not correlated (Figure 6), indicating that evaporation may have had only a minimal effect on the δ13CDIC-L value of the lake.
The preceding analysis demonstrates that the lake surface to atmospheric exchange of CO2 and evaporation had a relatively minimal effect on δ13CDIC-L; δ13CDIC-I primarily influenced the changes in δ13CDIC-L. The high photosynthetic efficiency of Chara spp. indicates that its corresponding δ13CDIC-L values showed a seasonal trend of more positive values, which were more positive than the δ13CDIC-L values of areas with vascular plants. This shows that the photosynthetic activity of vascular plants has a negligible effect on the δ13CDIC-L of lake water.

5.2. Isotopic Composition of DIC in the GGH Basin Groundwater and Shazhuyu River

The δ13CDIC-I and δ13CDIC-R values were significantly more negative than δ13CDIC-L during the same period, suggesting that isotopic fractionation due to atmospheric exchange or the photosynthesis of aquatic plants occurred after groundwater inflow into the lake [25]. Compared with the δ13CDIC-I value, δ13CDIC-R was significantly more positive, likely because the Shazhuyu River forms via surface runoff, as well as the more frequent exchange of atmospheric CO2 with surface water than groundwater.
A positive correlation was observed between δ13CDIC-I and δ13CDIC-R (n = 17, r2 = 0.46, and p < 0.01). Additionally, given that groundwater is the main water source for the GGH Lake, δ13CDIC-I was the main factor affecting the δ13CDIC-L values of the lake water (see Section 5.1). Therefore, we further analysed the influencing factors of δ13CDIC-I.
Three main species of DIC in water bodies are CO2, CO32−, and HCO3−. In this study, the titration method was used to determine the DIC composition of groundwater in the GGH Basin. HCO3− was the dominant form of DIC. Additional research has shown that the primary source of HCO3− in the groundwater of the Genggahai Basin and the Shazhuyu River is the chemical weathering of rocks, particularly carbonate rocks [37]. Based on the reaction equation for chemical weathering, CO2 is an essential component of this process. Dissolved CO2 in groundwater originates from the atmospheric flux, watershed soil respiration, and organic matter decomposition. Atmospheric CO2 normally has δ13C values of approximately −8‰ [25], which is relatively stable. The δ13C values of soil CO2 from areas with C3 and C4 plants range from −32 to −20‰ [38] and −17 to −19‰, respectively. The δ13C value of HCO3− from carbonate dissolution during subsurface weathering is approximately 0‰ [39]. The isotopic equilibrium between CO2 and HCO3− from different sources can be attained through the following exchange reaction:
13CO2(g) + H212CO3(aq) = 12CO2(g) + H213CO3(aq).
The fractionation factor of CO2 and HCO3− in the CO2–HCO3− system in soil is approximately 10‰ [25]. After soil CO2 is dissolved in water, its δ13C value becomes more negative than that of HCO3−. However, carbonate rock dissolution increases the δ13C value of HCO3−. The carbon source that affects the isotopic composition of DIC in groundwater encompasses two components: (1) 13C from the weathering and dissolution of carbonate rocks, which possesses a more positive isotope ratio; and (2) 12C from CO2 generated through soil respiration, which has a more negative isotope ratio. Therefore, the relative contribution of these two carbon sources to the groundwater DIC determines the composition of δ13CDIC-I in the GGH Basin.

5.3. Implications of Lake Water DIC Isotopic Composition on the Carbon Cycle

Lake ecosystems are active components of the global carbon cycle as they continually fix and release carbon through various biological processes, including photosynthesis, food web activity, and bacterial degradation [1,40]. The DIC and its isotopes are an important tool to elucidate the carbon cycle of lake ecosystems [10,11,18,30,41] (pp. 99–118). Striegl et al. [42] found that during the ice melting period, the average δ13CDIC-L value in 132 freshwater lakes in temperate and cold regions was −14‰. Similarly, Bade et al. [22] reported that the average δ13CDIC-L value in 108 freshwater lakes in different regions was −15‰. In freshwater lakes, the photosynthesis of aquatic plants and organic matter decomposition in sediments are active components of the carbon cycle [10]. In freshwater lakes on the Qiangtang Plateau, variations in δ13CDIC-L also showed that organic matter decomposition significantly contributed to the carbon cycle of the lake ecosystems [10]. However, Lei et al. [10] found that the δ13CDIC-L values of endorheic lakes on the Qiangtang Plateau were relatively high, approaching the δ13CDIC-L values of equilibrated CO2 in water to atmospheric exchange. Therefore, water surface to atmospheric CO2 exchange drives the carbon cycle in endorheic lakes on the Qiangtang Plateau [10].
The δ13CDIC-L values of the GGH Lake significantly exceeded the δ13CDIC values of freshwater lakes (>8‰) but were more negative than those of lakes on the Qiangtang Plateau (>−5.71‰) [10]. This indicates that the carbon cycle of the GGH Lake significantly differs from those of freshwater lakes and lakes on the Qiangtang Plateau. Variations in the δ13CDIC-L values of the GGH Lake indicate that organic matter decomposition and water–atmospheric CO2 exchange are not likely the main components of its carbon cycle (Section 5.1). Carbon input from inflowing groundwater and the photosynthesis of aquatic plants may be the main components of its carbon cycle.
Recent vegetation surveys have shown that the main terrestrial plants in the GGH watershed are Artemisia desertorum, Oxytropis aciphylla, Achnatherum splendens, Orinus kokonorica, and Agropyron cristatum. This is identical to that in the Qinghai Lake watershed [43]. The δ13Corg values of 3C plants in the Qinghai Lake watershed varied from −27.7 to −24.5‰ and those of soil in the Qinghai Lake watershed varied from −26.9 to −24.8‰ [43]. When the DIC in groundwater only originates from soil respiration and organic matter decomposition, if we neglect the effect of carbon isotope fractionation due to organic matter decomposition, the δ13CDIC of groundwater ranges from −27.7 to −24.5‰. Considering the isotope fractionation between CO2 (aq) and HCO3− (approximately −10‰) [8], the δ13CDIC of groundwater ranges from −17.7 to −14.5‰. As previously mentioned, most δ13CDIC values of groundwater in the GGH Lake watershed were more positive than −14.5‰. This indicates that soil respiration and organic matter decomposition may not be the only carbon source for the groundwater DIC in the watershed.
Significantly higher δ13CDIC (approximately −3 to +3‰) values in groundwater can occur in karstic regions where a proportion of the carbon atoms derive from the dissolution of catchment limestones [44]. For example, the δ13CDIC of groundwater from the Donggi Cona catchment on the north-eastern Qinghai–Tibetan Plateau varies from 0.9 to 2.0‰ [39]. Paleolake sediments that formed in the early and middle Pleistocene are widely distributed throughout the Gonghe Basin [14] (pp. 1–166). Here, HCO3− originates from the weathering of paleolake sediments and subsequent mineral dissolution, which is enriched in 13C. Groundwater flows transport the HCO3−, thus yielding more positive δ13CDIC values. We suggest that HCO3− originates from paleolake sediment weathering, which affects the groundwater DIC pool in the GGH watershed and yields relatively positive groundwater δ13CDIC values. Thus, variations in the δ13CDIC values of groundwater may reflect the relative contributions of two carbon sources to the groundwater DIC pool. One carbon source is CO2 that derives from soil respiration and organic matter decomposition while the other is HCO3− from paleolake carbonate sediments. Furthermore, we elucidated that variations in δ13CDIC-I may be affected by carbonate weathering and soil respiration related to vegetation succession. Therefore, variations in the δ13CDIC-L values of the GGH Lake may reflect the lake productivity and carbon cycle of the watershed. In the future, we should consider the carbon flux generated from aquatic plants photosynthesis, carbon sink capacity caused by rock weathering process in the watershed and carbon flux produced by soil respiration, when estimate the regional carbon budget.

6. Conclusions

(1)
For the overall DIC isotopic composition in the GGH Basin, we found that δ13CDIC-I was the most negative, followed by δ13CDIC-R; δ13CDIC-L was the most positive. This was caused by isotope fractionation resulting from the photosynthesis of aquatic plants after spring water inflow into the lake.
(2)
Owing to variations in the photosynthetic activity intensity of different aquatic plants, there were also significant variations in the δ13CDIC-L values in areas with different aquatic plants. This likely occurred because Chara spp. plants have a higher photosynthetic rate and are more capable of using CO2 for photosynthetic activity, converting them into plant organisms.
(3)
Variations in the δ13CDIC-L were primarily affected by the δ13CDIC-I and aquatic plant photosynthesis. The change in δ13CDIC-I to a more positive value resulted from carbon isotope equilibration between 13C from carbonate weathering in the watershed and 12CO2 from soil respiration.
(4)
The changes in the δ13CDIC-L composition of the GGH Lake indicated that the DIC from lake inflow and the photosynthesis of aquatic plants were the key components in the carbon cycle of the lake. This provides more supportive evidence to estimate the regional carbon budget and sustainable development.

Author Contributions

Conceptualization, Y.J.; methodology, Y.J.; formal analysis, Y.J.; investigation, Y.J.; resources, Y.J.; data curation Y.J.; writing—original draft preparation, Y.J.; writing—review and editing, X.J.; project administration, Y.J.; funding acquisition, X.J. and Y.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China [42201174], the Province Natural Science Foundation of Qinghai, China [2023-ZJ-728].

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Holgerson, M.A.; Raymond, P.A. Large contribution to inland water CO2 and CH4 emissions from very small ponds. Nat. Geosci. 2016, 9, 222. [Google Scholar] [CrossRef]
  2. Cole, J.J.; Caraco, N.F.; Kling, G.W.; Kratz, T.K. Carbon dioxide supersaturation in the surface waters of lakes. Science 1994, 265, 1568–1570. [Google Scholar] [CrossRef] [PubMed]
  3. Ran, L.; Lu, X.X.; Liu, S. Dynamics of riverine CO2 in the Yangtze River fluvial network and their implications for carbon evasion. Biogeosciences 2017, 14, 2183–2198. [Google Scholar] [CrossRef]
  4. Yan, F.; Sillanpää, M.; Kang, S.; Aho, K.S.; Qu, B.; Wei, D.; Raymond, P.A. Lakes on the Tibetan Plateau as conduits of greenhouse gases to the atmosphere. J. Geophys. Res.-Biogeosci. 2018, 123, 2091–2103. [Google Scholar] [CrossRef]
  5. Hanson, P.C.; Pollard, A.I.; Bade, D.L.; Predick, K.; Carpenter, S.R.; Foley, J.A. A model of carbon evasion and sedimentation in temperate lakes. Glob. Chang. Biol. 2004, 10, 1285–1298. [Google Scholar] [CrossRef]
  6. Balmer, M.B.; Downing, J.A. Carbon dioxide concentrations in eutrophic lakes: Undersaturation implies atmospheric uptake. Inland Waters 2011, 1, 125–132. [Google Scholar] [CrossRef]
  7. Wang, T.; Piao, S.L. Estimate of terrestrial carbon balance over the Tibetan Plateau: Progresses, challenges and perspectives. Quat. Sci. 2023, 43, 313–323, (In Chinese with English abstract). [Google Scholar]
  8. Zhang, J.; Quay, P.D.; Wilbur, D.O. Carbon isotope fractionation during gas-water exchange and dissolution of CO2. Geochim. Cosmochim. Acta 1995, 59, 107–114. [Google Scholar] [CrossRef]
  9. Myrbo, A.; Shapley, M.D. Seasonal water-column dynamics of dissolved inorganic carbon stable isotopic compositions (δ13CDIC) in small hardwater lakes in Minnesota and Montana. Geochim. Cosmochim. Acta 2006, 70, 2699–2714. [Google Scholar] [CrossRef]
  10. Lei, Y.; Yao, T.; Sheng, Y.; Zhang, E.; Wang, W.; Li, J. Characteristics of δ13CDIC in lakes on the Tibetan Plateau and its implications for the carbon cycle. Hydrol. Process. 2012, 26, 535–543. [Google Scholar] [CrossRef]
  11. Mu, C.; Zhang, T.; Wu, Q.; Peng, X.; Zhang, P.; Yang, Y.; Cheng, G. Dissolved organic carbon, CO2, and CH4 concentrations and their stable isotope ratios in thermokarst lakes on the Qinghai-Tibetan Plateau. J. Limnol. 2016, 75, 313–319. [Google Scholar] [CrossRef]
  12. Fang, Y.; Cheng, W.; Zhang, Y.; Wang, N.; Zhao, S.; Zhou, C.; Bao, A. Changes in inland lakes on the Tibetan Plateau over the past 40 years. J. Geogr. Sci. 2016, 26, 415–438. [Google Scholar] [CrossRef]
  13. Zhang, G.; Yao, T.; Chen, W.; Zheng, G.; Shum, C.K.; Yang, K.; Piao, S.; Sheng, Y.; Yi, S.; Li, J. Regional differences of lake evolution across China during 1960s-2015 and its natural and anthropogenic causes. Remote Sens. Environ. 2019, 221, 386–404. [Google Scholar] [CrossRef]
  14. Dong, G.R.; Gao, S.Y.; Jin, J. Desertification and Controls in the Gonghe Basin, Qinghai Province; Science Press: Beijing, China, 1993; pp. 1–166. [Google Scholar]
  15. Winkler, M.G.; Wang, P.K. The late-Quaternary vegetation and climate of China. In Global Climates Since the Last Glacial Maximum; Wright, H.E., Jr., Kutzbach, J.E., Webb, T., III, Ruddiman, W.F., Street-Perrott, F.A., Bartlein, P.J., Eds.; University of Minnesota Press: Minneapolis, MN, USA, 1993; pp. 221–264. [Google Scholar]
  16. Qiang, M.; Song, L.; Chen, F.; Li, M.; Liu, X.; Wang, Q. A 16-ka lake-level record inferred from macrofossils in a sediment core from Genggahai Lake, northeastern Qinghai–Tibetan Plateau (China). J. Paleolimnol. 2013, 49, 575–590. [Google Scholar] [CrossRef]
  17. Li, Y.; Qiang, M.; Jin, Y.; Liu, L.; Zhou, A.; Zhang, J. Influence of aquatic plant photosynthesis on the reservoir effect of Genggahai Lake, northeastern Qinghai-Tibetan Plateau. Radiocarbon 2018, 60, 561–569. [Google Scholar] [CrossRef]
  18. Wachniew, P.; Różański, K. Carbon budget of a mid-latitude, groundwater-controlled lake: Isotopic evidence for the importance of dissolved inorganic carbon recycling. Geochim. Cosmo-Chim. Acta 1997, 61, 2453–2465. [Google Scholar] [CrossRef]
  19. Song, L.; Qiang, M.; Lang, L.; Liu, X.; Wang, Q.; Li, M. Changes in palaeoproductivity of Genggahai Lake over the past 16 ka in the Gonghe Basin, northeastern Qinghai-Tibetan Plateau. Chin. Sci. Bull. 2012, 57, 2595–2605. [Google Scholar] [CrossRef]
  20. Jin, Y.; Qiang, M.; Liu, Y.; Li, Y.; Li, H.; Li, F. Variations in carbon and oxygen isotopes of carbonate and water environments: A case study from Genggahai Lake, northeastern Qinghai-Tibetan Plateau. Chin. Sci. Bull. 2015, 60, 847–856. (In Chinese) [Google Scholar]
  21. Van den Berg, M.S.; Coops, H.; Simons, J.; Pilon, J. A comparative study of the use of inor-ganic carbon resources by Chara aspera and Potamogeton pectinatus. Aquat. Bot. 2002, 72, 219–233. [Google Scholar] [CrossRef]
  22. Bade, D.L.; Carpenter, S.R.; Cole, J.J.; Hanson, P.C.; Hesslein, R.H. Controls of δ13C-DIC in lakes: Geochemistry, lake metabolism, and morphometry. Limnol. Oceanogr. 2004, 49, 1160–1172. [Google Scholar] [CrossRef]
  23. Shen, J.; Yuan, H.; Liu, E.; Wang, J.; Wang, Y. Spatial distribution and stratigraphic character-istics of surface sediments in Taihu Lake. China Sci. Bull. 2011, 56, 179–187. [Google Scholar] [CrossRef]
  24. Deuser, W.G.; Degens, E.T. Carbon isotope fractionation in the system CO2(gas)—CO2(aqueous)—HCO3−(aqueous). Nature 1967, 215, 1033. [Google Scholar] [CrossRef]
  25. Leng, M.J.; Marshall, J.D. Palaeoclimate interpretation of stable isotope data from lake sediment archives. Quat. Sci. Rev. 2004, 23, 811–831. [Google Scholar] [CrossRef]
  26. Das, A.; Krishnaswami, S.; Bhattacharya, S.K. Carbon isotope ratio of dissolved inorganic carbon (DIC) in rivers draining the Deccan Traps, India: Sources of DIC and their magnitudes. Earth Planet. Sci. Lett. 2005, 236, 419–429. [Google Scholar] [CrossRef]
  27. Stumm, W.; Morgan, J.J. Aquatic Chemistry: An Introduction Emphasizing Chemical Equilibria in Natural Waters, 3rd ed.; Wiley: New York, NY, USA, 1970; p. 583. [Google Scholar]
  28. Sun, Q.; Xie, M.; Shi, L.; Zhang, Z.; Lin, Y.; Shang, W.; Chu, G. Alkanes, compound-specific carbon isotope measures and climate variation during the last millennium from varved sediments of Lake Xiaolongwan, northeast China. J. Paleolimnol. 2013, 50, 331–344. [Google Scholar] [CrossRef]
  29. McKenzie, J.A. Carbon-13 Cycle in Lake Greifen: A Model for Restricted Ocean Basins. In Nature and Origin of Cretaceous Carbon-Rich Facies; Schlanger, S.O., Cita, M.B., Eds.; Academic Press: London, UK, 1982; pp. 197–207. [Google Scholar]
  30. McKenzie, J.A. Carbon Isotopes and Productivity in the Lacustrine and Marine Environment. In Chemical Processes in Lakes; Stumm, W., Ed.; Wiley: New York, NY, USA, 1985; pp. 99–118. [Google Scholar]
  31. Andrews, J.E.; Coletta, P.; Pentecost, A.; Riding, B.; Dennis, S.; Dennis, P.F.; Spiro, B. Equilibrium and disequilibrium stable isotope effects in modern charophyte calcites: Implications for palaeoenvironmental studies. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2004, 204, 101–114. [Google Scholar] [CrossRef]
  32. Pełechaty, M.; Apolinarska, K.; Pukacz, A.; Krupska, J.; Siepak, M.; Boszke, P.; Sinkowski, M. Stable isotope composition of Chara rudis incrustation in Lake Jasne, Poland. Hydrobiologia 2010, 656, 29–42. [Google Scholar] [CrossRef]
  33. Herczeg, A.L.; Fairbanks, R.G. Anomalous carbon isotope fractionation between atmospheric CO2 and dissolved inorganic carbon induced by intense photosynthesis. Geochim. Cosmochim. Acta 1987, 51, 895–899. [Google Scholar] [CrossRef]
  34. Pentecost, A.; Andrews, J.E.; Dennis, P.F.; Marca-Bel, A.; Dennis, S. Charophyte growth in small temperate water bodies: Extreme isotopic disequilibrium and implications for the palaeoecology of shallow marl lakes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2006, 240, 389–404. [Google Scholar] [CrossRef]
  35. Li, H.C.; Ku, T.L. δ13C–δ18C covariance as a paleohydrological indicator for closed-basin lakes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 1997, 133, 69–80. [Google Scholar] [CrossRef]
  36. Qiang, M.; Song, L.; Jin, Y.; Li, Y.; Liu, L.; Zhang, J.; Chen, F. A 16-ka oxygen-isotope record from Genggahai Lake on the northeastern Qinghai-Tibetan Plateau: Hydroclimatic evolution and changes in atmospheric circulation. Quat. Sci. Rev. 2017, 162, 72–87. [Google Scholar] [CrossRef]
  37. Jin, Y.; Jin, X.; Yang, D.; Mao, X. Controlling Factors of Surface Water Ionic Composition Characteristics in the Lake Genggahai Catchment, NE Qinghai–Tibetan Plateau, China. Water 2019, 11, 1329. [Google Scholar] [CrossRef]
  38. Brunet, F.; Gaiero, D.; Probst, J.L.; Depetris, P.J.; Gauthier Lafaye, F.; Stille, P. δ13C tracing of dissolved inorganic carbon sources in Patagonian rivers (Argentina). Hydrol. Process. 2005, 19, 3321–3344. [Google Scholar] [CrossRef]
  39. Weynell, M.; Wiechert, U.; Zhang, C. Chemical and isotopic (O, H, C) composition of surface waters in the catchment of Lake Donggi Cona (NW China) and implications for paleoenvironmental reconstructions. Chem. Geol. 2016, 435, 92–107. [Google Scholar] [CrossRef]
  40. Tranvik, L.J.; Downing, J.A.; Cotner, J.B.; Loiselle, S.A.; Striegl, R.G.; Ballatore, T.J.; Dillon, P.; Finlay, K.; Fortino, K.; Knoll, L.B.; et al. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol. Oceanogr. 2009, 54, 2298–2314. [Google Scholar] [CrossRef]
  41. Herczeg, A.L.; Leaney, F.W.; Dighton, J.C.; Lamontagne, S.; Schiff, S.L.; Telfer, A.L.; English, M.C. A modern isotope record of changes in water and carbon budgets in a groundwater-fed lake: Blue Lake, South Australia. Limnol. Oceanogr. 2003, 48, 2093–2105. [Google Scholar] [CrossRef]
  42. Striegl, R.G.; Kortelainen, P.; Chanton, J.P.; Wickland, K.P.; Bugna, G.C.; Rantakari, M. Carbon dioxide partial pressure and 13C content of north temperate and boreal lakes at spring ice melt. Limnol. Oceanogr. 2001, 46, 941–945. [Google Scholar] [CrossRef]
  43. Liu, W.; Li, X.; An, Z.; Xu, L.M.; Zhang, Q.L. Total organic carbon isotopes: A novel proxy of lake level from Lake Qinghai in the Qinghai-Tibet Plateau, China. Chem. Geol. 2013, 347, 153–160. [Google Scholar] [CrossRef]
  44. Andrews, J.E.; Riding, R.; Dennis, P.F. The stable isotope record of environmental and climatic signals in modern terrestrial microbial carbonates from Europe. Palaeogeogr. Palaeoclimatol. Palaeoecol. 1997, 129, 171–189. [Google Scholar] [CrossRef]
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Figure 1. Setting and location. (a) Location of the Gonghe Basin (Google EarthTM). The red rectangle and red circle indicate the study site. Climate circulation systems influencing the study area are also shown. Dashed and solid lines indicate the modern extent of the East Asian summer monsoon (EASM) and Indian Ocean summer monsoon (ISM), respectively [15] (pp. 221–264). (b) GGH Lake, its surrounding physical environments, and the spatial distribution of modern aquatic vegetation [16].
Figure 1. Setting and location. (a) Location of the Gonghe Basin (Google EarthTM). The red rectangle and red circle indicate the study site. Climate circulation systems influencing the study area are also shown. Dashed and solid lines indicate the modern extent of the East Asian summer monsoon (EASM) and Indian Ocean summer monsoon (ISM), respectively [15] (pp. 221–264). (b) GGH Lake, its surrounding physical environments, and the spatial distribution of modern aquatic vegetation [16].
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Figure 2. Seasonal variations in the physical and chemical parameters of the lake water in different plant growth areas of the GGH Lake from May to September (2012–2015). (a) Water temperature; (b) pH value; and (c) DO concentrations.
Figure 2. Seasonal variations in the physical and chemical parameters of the lake water in different plant growth areas of the GGH Lake from May to September (2012–2015). (a) Water temperature; (b) pH value; and (c) DO concentrations.
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Figure 3. Variations in the carbon isotopes of DIC precipitated from the (a) surface water in the GGH Lake and (b) waterbodies within its catchment from May to September (2012–2015).
Figure 3. Variations in the carbon isotopes of DIC precipitated from the (a) surface water in the GGH Lake and (b) waterbodies within its catchment from May to September (2012–2015).
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Figure 4. Annual variations in the carbon isotopes of DIC precipitated from the surface water in the GGH Lake (a) and waterbodies within its catchment (b) from 2012 to 2015.
Figure 4. Annual variations in the carbon isotopes of DIC precipitated from the surface water in the GGH Lake (a) and waterbodies within its catchment (b) from 2012 to 2015.
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Figure 5. Correlation between the carbon isotopes of inflowing water DIC and lake water DIC in (a) Chara spp., (b) Potamogeton pectinatus, and (c) Myriophyllum spicatum growth areas.
Figure 5. Correlation between the carbon isotopes of inflowing water DIC and lake water DIC in (a) Chara spp., (b) Potamogeton pectinatus, and (c) Myriophyllum spicatum growth areas.
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Figure 6. Correlation between the carbon isotopes of the lake water DIC and oxygen isotopes of the lake water in the (a) Chara spp., (b) Potamogeton pectinatus, and (c) Myriophyllum spicatum growth areas.
Figure 6. Correlation between the carbon isotopes of the lake water DIC and oxygen isotopes of the lake water in the (a) Chara spp., (b) Potamogeton pectinatus, and (c) Myriophyllum spicatum growth areas.
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Table 1. The detailed list of water samples and measured parameters.
Table 1. The detailed list of water samples and measured parameters.
Water Sample TypesMeasured Parameters
Lake water
Groundwater
River water		Water temperature
pH
DO
carbon isotopes
oxygen isotopes
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Jin, Y.; Jin, X. Variations in δ13CDIC and Influencing Factors in a Shallow Macrophytic Lake on the Qinghai–Tibetan Plateau: Implications for the Regional Carbon Cycle and Sustainable Development. Sustainability 2024, 16, 3350. https://doi.org/10.3390/su16083350

AMA Style

Jin Y, Jin X. Variations in δ13CDIC and Influencing Factors in a Shallow Macrophytic Lake on the Qinghai–Tibetan Plateau: Implications for the Regional Carbon Cycle and Sustainable Development. Sustainability. 2024; 16(8):3350. https://doi.org/10.3390/su16083350

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Jin, Yanxiang, and Xin Jin. 2024. "Variations in δ13CDIC and Influencing Factors in a Shallow Macrophytic Lake on the Qinghai–Tibetan Plateau: Implications for the Regional Carbon Cycle and Sustainable Development" Sustainability 16, no. 8: 3350. https://doi.org/10.3390/su16083350

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