## 4 Discussion  
### The Influence of Factors on CO\({}_{2}\) Exchange Flux  
Temperature variations throughout the daytime have an impact on the CO\({}_{2}\) flux [13,19]. At Q1, there is a weak correlation between most parameters with fewer submerged plants and low water flow. The correlation between CO\({}_{2}\) flux and DIC (r = 0.41, \(p\) = 0.19), conductivity (r = 0.52, \(p\) = 0.08), and temperature (r = 0.47, \(p\) = 0.12) is significant and positively correlated. However, the correlation between CO\({}_{2}\) flux and temperature is negative at H1 (r = \(-\)0.39, \(p\) = 0.21) and H2 (r = \(-\)0.59, \(p\) = 0.04). At Q1, the correlation between air temperature and water temperature is small, while at the river section, there is a strong correlation between air temperature and water temperature. Submerged plants thrive between H1 and H2. The correlation coefficient distribution map (Figure 5) shows that the correlation between various parameters and CO\({}_{2}\) flux is more significant for H2 than for H1, indicating that river submerged plants are closely involved in the process of river carbon absorption and release as well as the alteration of water chemistry and physical indicators. Figure 4: Changes in CO\({}_{2}\) flux at different points in the open-flow section (note: The gray portion represents the period of night during the sampling period in the study area, while the white portion represents the period of daytime). The solubility of CO2 in water influences the amount of CO2 released from streams and is negatively correlated to water temperature [33]. The H2 has a higher temperature than the H1, as shown in Figure 2. Theoretically, the CO2 flux will increase as CO2 solubility decreases. However, the water temperature is negatively correlated with the CO2 flux of the H1 (r = \(-\)0.41, \(p\) = 0.18) and H2 (r = \(-\)0.67, \(p\) = 0.02) (Figure 6a), which is consistent with the conclusion from the Lijiang River section [34]. In these two sections, water temperature shows a clear trend of higher temperature during the day and lower temperature at night (similar to the trend of temperature change) (Figure 6b), but the amplitude of change is not significant. Therefore, there are some other processes more influential than temperature in controlling CO2 release during river flow. Figure 5: Correlation of parameters at different points in the open-flow section (note: 1. (**A**–**C**) represent the distribution of correlation coefficients between various parameters at the underground river outlet (Q1), river section without submerged plants (H1), and river section with submerged plants (H2), respectively. 2. The shadow depth in the upper triangle area represents the correlation strength between different elements with red indicating a positive correlation between elements and blue indicating a negative correlation between elements. The darker the color is, the higher the saturation and the more significant the element correlation). Changes in DO and CO\({}_{2}\) concentrations in river water are directly influenced by the metabolic activities of submerged plants and microbial communities (photosynthesis and  
Figure 6: Response relationship between CO\({}_{2}\) flux and hydrochemical parameters (note: (**a–h**) are the results of the fitted line and correlation analysis). respiration), and variations in \(p\)CO\({}_{2}\) also affect pH levels [35; 36]. Changes occurring during the day and night usually lead to periodic diurnal variations in CO\({}_{2}\) degassing at the water-air interface [37]. Throughout this study, the relationship between \(p\)CO\({}_{2}\) and CO\({}_{2}\) flux was consistently positive. The study demonstrated an enhanced connection between \(p\)CO\({}_{2}\) and CO\({}_{2}\) flux at all points (Figure 6c), indicating that metabolic processes largely control CO\({}_{2}\) release in the Heilongtan River.