Spatial-temporal distribution characteristics of nitrogen in the Jingjiang reach of the Yangtze River affected by the operation of Three Gorges Reservoir

Spatial-temporal distribution characteristics of nitrogen in the Jingjiang reach of the Yangtze River affected by the operation of Three Gorges Reservoir | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial-temporal distribution characteristics of nitrogen in the Jingjiang reach of the Yangtze River affected by the operation of Three Gorges Reservoir Yihao Wu, Yuhong Zeng, Runpei Liu, Xiaoning Liu, Bao Qian, Li Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8410924/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The regulation of water storage in the Three Gorges Reservoir (TGR) has altered the migration and transformation of nutrients in the middle and lower reaches of the Yangtze River. In this study, long-term hydrological and water quality monitoring data (before and after the operation of the TGR) for Yichang, Zhicheng, Shashi, and Chenglingji along the Jingjiang Reach in the middle-stream of Yangtze River were analyzed, and the distribution characteristics of nitrogen (N) in overlying water were quantified. The results shows that the annual average total nitrogen (TN) concentration in the Jingjiang Reach increased from 1992 to 2015, while the ammonium nitrogen (NH4+-N) concentrations showed a downward trend. The variation trends of both N forms shifted around 2003 when TGR begins to storage water. Seasonal variations were observed, with the highest concentrations of TN and nitrate nitrogen (NO3–N) occurring during the normal water period (March-May). During this period, the average TN concentration across the four monitoring sites was 33% and 19.8% higher than those during the dry and wet periods, respectively. In contrast, the peak concentrations of NH4+-N was observed during the dry period (October-February). Additionally, correlation analysis and Redundancy Analysis (RDA) revealed that Chemical Oxygen Demand (COD) and the distance from the TGR were the most influential factors on N concentrations in the Jingjiang Reach, with corresponding contribution rates of 36.8% and 32.5%, and the distance from TGR exerted a significant positive effect on NH4+-N concentration, while COD showed a significant negative effect on TN concentration. The results suggested that the impoundment of the TGR has had a significant impact on the N content in the Jingjiang Reach, providing a theoretical support for nutrient management in the Yangtze River. Jingjiang Reach of the Yangtze River temporal and spatial distribution of nitrogen water quality Three Gorges Reservoir Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction As one of the world’s largest hydroelectric and water conservancy projects, the construction and operation of the TGR have exerted profound and multidimensional impacts on the hydrological regime and water quality in the lower reaches of the Yangtze River, with particularly prominent effects on the biogeochemical cycling of nutrients, most notably Nitrogen(N) cycling dynamics (Wang et al. 2020 ; Li et al. 2021). The TGR’s regulation of the Yangtze River directly alters key hydrological processes in its downstream reaches, such as the natural flow regime, as well as the spatiotemporal distribution of nutrients like nitrogen (Yang et al. 2022 ). Although the TGR delivers substantial socioeconomic benefits, its unintended impacts on N cycling and downstream water quality pose significant challenges to integrated river basin management (Nie et al. 2021 ). Water and sediment are the main carriers of nutrients (Wijesiri et al., 2019 ; Tang et al., 2023 ). The change of discharge-sediment flux relationship caused by the operations of hydraulic engineering also has an important impact on the flux of nutrients (Gong et al., 2015 ; Yin et al., 2023 ). Specially, the operation of reservoirs can significantly affect river runoff and sediment transport processes (Wang and Wang, 2023 ; Sun et al., 2022 ), which in turn will also have an impact on the water environment of downstream rivers (Andualem et al., 2023 ; Hamidifar et al., 2024). It has been proved that the construction and operation of dams can alter the seasonal characteristics of N flux and transformation in river networks (Yang et al., 2025 ; Gan et al., 2025). The migration and transformation of N, as well as its chemo-biological reaction processes, are susceptible to various environmental factors (He et al., 2025 ; Bao et al., 2024 ), which leads to that the morphological transformation and distribution of N are complex and unpredictable (Wang et al., 2023 ; Gan et al., 2025). Most existing studies on N concentrations and other hydrological quality indices in the Jingjiang Reach have been conducted within a relatively short monitoring period. There remains a lack of in-depth analysis on various water body indices in this reach over an extended time span. Previous studies showed that the temporal and spatial distribution characteristics and change trends of hydrological and water quality indexes in the Jingjiang River reach may lead to changes in the forms of N and affect the water quality conditions (He et al., 2025 ). For instance, seasonal differences in hydrological conditions can lead to seasonal variations in N concentrations in river waters (Li et al., 2025 ; Gong et al., 2015 ). However, previous studies mostly focused on the temporal changes of a single environmental variable, but not enough on the spatial changes especially under the effect of the regulation of the TGR. Water pollution associated with rapid economic development along large rivers has received considerable attention in which eutrophication is one of the greatest threats to surface water worldwide (Huang et al., 2021 ; Gao, 2024). As an essential component of living organisms and the main nutrients, N is closely related to the pollution conditions and trophic status of the water bodies (Jiang and Nakano, 2022; Kakade et al., 2021 ). With the gradual urbanization in the Yangtze River Basin, the N content in the Jingjiang Reach of the middle stream of Yangtze river has an increasing trend (Cui et al., 2023 ; Zhang et al., 2021 ). There is a lack of systematic research on the distribution characteristics of N forms and N concentrations in the water body of the Jingjiang Reach. Regarding the gaps of previous studies, this paper analyzed the distribution characteristics of various forms of N in the water body of Jingjiang Reach, discussed their relationships with hydrological and other water quality indicators based on a long term hydrological and water quality data collected from four hydrological stations set along the Jingjiang Reach. Lastly, the effects of the TGR were comparatively analyzed and clarified. It is anticipated that the results will provide a new understanding on the environment effect of TGR operation. 2 Materials and methods 2.1 Research area The Jingjiang Reach meanders through the flat Jianghan Plain of Hubei Province, China, which is an important part of the waterway of the main stream of the Yangtze River. With an average annual rainfall ranging from about 1000 to 1500 millimeters, and an average annual temperature around 16 to 18 degrees Celsius, the Jingjiang Reach has received extensive attention due to its ecological value, since it involves four historically recorded spawning grounds for the "Four Major Chinese Carps", and seven nature reserves, for Yangtze finless porpoise and other important and commercial fish species. The Jingjiang Reach (from Zhijiang to Chenglingji), located downstream of the TGR, is significantly affected by the operation of reservoir in terms of its hydrological rhythm. Generally, the impoundment of the TGR is divided into three stages (S-Ⅰ, S-Ⅱ and S-Ⅲ) according the storage level, with corresponding occurrence time in 2003 (135 m), 2006 (156 m) and 2010 (175 m) respectively. During the operation of the TGR, different dispatching strategies are adopted according to different water periods: during the post-flood storage period (October - December), the water level is stored to the normal storage level of 175 meters; in the dry season (January - May), water is gradually released to meet the needs of the lower reaches; during the wet season (June - September), a relatively low water level of about 145 meters (flood control limited water level) is maintained to reserve storage capacity for flood control. The operation of the TGR has significantly altered the sediment transport and flow discharge of the Jingjiang Reach. From 1990 to 2021, the annual mean runoff of the Jingjiang Reach remained stable over the long term, ranging between 350 and 400 billion cubic meters (km³). From an intra-annual perspective, the proportion of runoff occurring during the flood season decreased from 70% in the period 1990–2002 to 65% in 2003–2021. The sediment concentration in the Jingjiang Reach exhibited a "stepwise decreasing" trend. Taking 1992–2002 as the pre-impoundment period (baseline), the sediment concentration decreased by 53.4% during the first-stage impoundment (2003–2008), by 85.5% during the second-stage impoundment (2009–2012) and by 90.4% during the third-stage impoundment (2013–2021). These results demonstrate the significant sediment-trapping effect of the TGR (Guo et al., 2023 ). 2.2 Data sources Four national hydrological stations in the middle stream of Yangtze River, namely Yichang, Zhicheng, Shashi, and Chenglingji, which are 42.5 km, 90 km, 187 km, and 420 km away from the TGR respectively were selected. Among them, the Yichang station is the outlet station of the TGR and serves as an important station connecting the upstream reservoir and the downstream Jingjiang Reach; the Zhicheng station is the starting point of the Jingjiang River section; and the Chenglingji station is the endpoint of the Jingjiang River section. Long-term data on N forms concentrations (1992–2015) were collected from hydrological bureaus and water quality monitoring platforms at four stations (Yichang, Zhicheng, Shashi, and Chenglingji). These data included concentrations of nitrite nitrogen (NO 2 − -N, mg/L), nitrate nitrogen (NO 3 − -N, mg/L), ammonium nitrogen (NH 4 + -N, mg/L), and total nitrogen (TN, mg/L). Given that the TGR underwent three stages of impoundment in 2003, 2006, and 2008–2010, respectively, the 1992–2015 dataset effectively captures the impacts of TGR construction and impoundment processes on N concentration variations in the Jingjiang Reach. Additionally, other hydrological and water quality parameters (1992–2000) were collected for each station, including water level ( Z , m), discharge ( Q , m³/s), water temperature ( T , ℃), pH, electrical conductivity ( EC , µS/cm), oxidation-reduction potential ( ORP , mV), and dissolved oxygen ( DO , mg/L).This comprehensive dataset provides a robust basis for analyzing spatiotemporal patterns of N dynamics and their responses to hydrological modifications induced by the TGR. 3 Results and discussion 3.1 Spatial distribution of N concentrations in water Figure 1 illustrates the longitudinal variation trend of annual mean TN concentrations along the Jingjiang Reach from 1992 to 2015. At all the monitoring stations, TN consistently exceeds the value for class Ⅱ (0.5 mg L − 1 ) of the Chinese guideline (GB3838 2002), notably, the TN concentrations at Zhicheng station and Shashi station have consistently exceeded the allowed limit (class Ⅲ, 1.0 mg L − 1 ) across all observed years. Within the observation period, some monitoring values at all four stations met the national Class IV surface-water threshold (1.5 mg L − 1 ). Notably, the water quality data of the Zhicheng and Shashi stations from 2013 to 2015 even reached the national Class V surface-water threshold (2.0 mg L − 1 ), the highest (most polluted) class in the national surface water quality classification system. An analysis of the multi-year average TN concentration reveals a distinct trend along the flow direction: it generally increases initially and then decreases. Specifically, the TN concentration starts at 1.378 mg/L at the Yichang station, rises to 1.665 mg/L at the Zhicheng station, and finally declines to 1.110 mg/L at the Chenglingji station. Relative to the Yichang station, the TN concentration at the Chenglingji station shows a net decrease of 19.4%. Notably, there is no significant difference in TN concentration between the Shashi station and the Zhicheng station. The spatial distribution of the proportion of N in various forms is shown in Fig. 2 . The proportion of NO 2 − -N at the four stations is all lower than 5%. The proportion of NH 4 + -N gradually increased from 15% in Yichang station to 31% in Chenglingji station, while the proportion of NO 3 − -N decreased from 82% in Yichang station to 66% in Chenglingji station. No obvious spatial variation trend was observed in the proportion of NO 2 − -N among the four monitoring sites, which may be associated with the generally low concentration of NO 2 − -N. The proportion of NH 4 + -N increased along the reach, while that of NO 3 − -N decreased longitudinally. Two potential mechanisms underlying this pattern are proposed as follows: first, the annual mean water temperature at the four monitoring stations ranged approximately from 15 ℃ to 20 ℃. Within this temperature range, the activity of ammonifying bacteria is relatively high, whereas the activity of nitrifying bacteria remains low. This imbalance between ammonification and nitrification can lead to the accumulation of NH 4 + -N and a relative reduction in NO 3 − -N along the reach. Second, increasing anthropogenic disturbances along the Jingjiang Reach may have directly elevated the input of NH 4 + -N into the water body, thereby increasing its proportion relative to other N forms. 3.2 Temporal distribution of N concentrations in water 3.2.1 Interannual distribution characteristics As shown in Fig. 3 , the growth trend of TN for each station is obvious from 1992 to 2003, and the variation curve is relatively flat from 2003 to 2009. Considering that 2003 is the time node of the first stage of the water storage of TGR, it is indicating that the dam construction has a certain impact on the N distribution in the water body of the Jingjiang Reach. At the same time, the TN concentration of Chenglingji station and Yichang station decreased slightly from 2015 to 2021, while the TN concentration of Zhicheng station and Shashi station have no obvious change. According to Figs. 3 and 4 , the concentration of NH 4 + -N in the Jingjiang Reach fluctuates with an overall decreasing trend. Over an extended time span, the annual mean N concentration in the Jingjiang Reach has increased year by year, with the issue of N pollution worsening gradually. Notably, taking 2003 as a temporal node, the increasing trend of annual mean TN concentration slowed down, while the variation trend of annual mean NH 4 + -N concentration shifted. It indicates that the TGR may exert a certain impact on the N distribution in the water body of the Jingjiang Reach. 3.2.2 Seasonal distribution characteristics The N concentration in the Jingjiang Reach also showed certain seasonal differences. As shown in Fig. 5 , except for Chenglingji station, the TN concentration of the other three stations was the highest in the normal water period (from March to May), and the seasonal distribution trend of NO 3 − -N was similar to that of TN. The NH 4 + -N concentrations in Zhicheng, Shashi and Chenglingji stations were the highest during the dry season (from October to February), which was affected by seasonal factors to a certain extent. Significance test by SPSS shows that this change was not obvious (the p value was larger than 0.05). 3.2.3 Distribution characteristics of different water impoundment stages Single-factor Analysis of Variance (ANOVA) is a statistical tool employed to determine whether the differences in mean values among three or more groups are statistically significant. In this study, single-factor ANOVA was applied to analyze two sets of key affecting parameters: one is the seasonal variations of environmental or hydrological parameters in the Jingjiang Reach of the Yangtze River; and the other is the variations of these parameters across different impoundment stages of the TGR. Through this analytical approach, the statistical significance of changes in the target parameters was tested, providing a quantitative basis for verifying the magnitude of parameter fluctuations induced by seasonal dynamics and reservoir operation. The distribution characteristics of different forms of N in the water bodies of each station in the Jingjiang Reach during different impoundment stages of the TGR are shown in Fig. 6 . The results of the one-way analysis of variance for the distribution of N concentrations at different stages are shown in Table 1 . It was found that the three-stage impoundment process of the TGR exerted a significant impact on the variation trends of TN, NO 3 − -N and NH 4 + -N concentrations at the Yichang and Zhicheng stations ( p 0.05). Similarly, the concentrations of TN, NO 3 − -N, and NO 2 − -N at the Shashi station were all significantly affected by the TGR impoundment to a certain extent. At the Chenglingji station, the concentrations of TN and NH 4 + -N in the water body were also affected by the reservoir impoundment, with a relatively significant degree of influence. Table 1 One-way analysis of variance of N concentration distribution characteristics at different water storage stages Station Stage changes ( p < 0.05) Yichang TN、NO 3 − -N、NH 4 + -N Zhicheng NO 3 − -N、TN、NH 4 + -N Shashi TN、NO 2 − -N、NO 3 − -N Chenglingji NH 4 + -N、TN The concentrations of TN and NH 4 + -N in the water body of Chenglingji station are also significantly affected by the water storage of TGR(Fig. 6 ). The TN concentration at Chenglingji station increased from 1.5 mg/L in the first stage to 1.848 mg/L in the third stage, with an overall increase of 23%. The TN concentration of the other stations also continued to increase from the first to the third stage. The NH 4 + -N concentration at Yichang station decreased from 0.12 mg/L in the first stage to 0.078 mg/L in the third stage. These results indicate that the impoundment of water in the TGR affects the distribution characteristics of N in the water body of the Jingjiang Reach and the proportion of N in various forms to a certain extent. At the same time, it was also found that NO 2 − -N is less affected by the water storage in the reservoir area. On the one hand, it is due to the unstable state of NO 2 − -N in the water body, which is easy to convert into other forms of N. On the other hand, it may be due to the low concentration of NO 2 − -N in the water body of the Jingjiang Reach, and some of the data are lower than the detecting limit during the actual measurement. The distribution characteristics of N concentrations across different impoundment stages of the TGR revealed that the operation of the TGR has altered the hydrological regime of the downstream Jingjiang Reach. Concurrently, it has modified the spatiotemporal distribution of various N forms in the river and may have effect on the fluvial ecosystem—exerting significant impacts on N transport and transformation processes. Therefore, in general, the effects of the TGR’s staged impoundment mode on N concentrations and N forms require special attention in river basin management and ecological protection practices. 3.3 Effect of Three Gorges Reservoir on N distribution in Jingjiang Reach 3.3.1 Variations of Hydrological and Water Quality Parameters The transformation and transport of N in aquatic systems are governed by a complex interplay of physical, chemical, and biological processes. Furthermore, the operation of the TGR has exerted a considerable influence on various hydrological and water quality parameters. Therefore, a detailed investigation into the variations of these parameters is essential. Such an analysis is crucial for elucidating the distribution patterns of N concentrations and holds significant implications for subsequent research. The Jingjiang Reach, spanning approximately 360 km, exhibits a substantial spatial gradient from upstream to downstream. This is reflected in pronounced spatial disparities in water level, discharge, water temperature, EC, pH, ORP, DO, COD, and BOD 5 at monitoring stations along the reach. As shown in Fig. 7 , from 1992 to 2000, the multi-year average water level was highest at the Yichang station and gradually decreased along the flow direction, reaching 24.70 m at the Chenglingji station. The spatial distribution of discharge followed a similar pattern, with multi-year averages of 13,270.93 m 3 /s (Yichang), 13,408.45 m 3 /s (Zhicheng), 12,632.29 m 3 /s (Shashi), and 6,815.95 m 3 /s (Chenglingji). The multi-year average water temperature exhibited an initial decrease followed by an increase along the river course, although the overall trend was not pronounced. In Fig. 8 , the averaged EC, pH, ORP, DO, COD, and BOD 5 from 1992 to 2000 of the four sites have been illustrated. In contrast, the average EC demonstrated a clear decreasing trend from 339.9 µS/cm at Yichang to 265.3 µS/cm at Chenglingji, with a minimal difference observed between Zhicheng and Shashi. The average ORP at Yichang was relatively low, differing significantly from the other three stations. ORP generally increased downstream, suggesting an enhancement in the oxidizing capacity of the water body along the Jingjiang Reach. The multi-year average DO concentration decreased from 8.82 mg/L at Yichang to 8.50 mg/L and 8.51 mg/L at Zhicheng and Shashi, respectively, before increasing to 8.81 mg/L at Chenglingji. The spatial distributions of COD and BOD 5 concentrations were similar to that of DO, all displaying a pattern of higher values at the terminal stations and lower values in the middle section, albeit with a less pronounced trend for BOD 5 . Overall, the multi-year average DO concentrations along the Jingjiang Reach met the Class I water quality standard (> 7.5 mg/L), as did the COD and BOD 5 levels. The tri-oxygen parameters (COD, BOD 5 , DO) demonstrate that during the three construction and operation phases of the TGR (1992–2000), organic pollution in the Jingjiang Reach decreased, and water quality improved to some extent. 3.3.2 Correlation analysis Kendall's τ correlation coefficient is a non-parametric statistical metric employed to quantify the nonlinear relationship between two variables. In this section, Kendall's τ correlation analysis was applied to long-term time-series monitoring data of multiple hydrological and water quality parameters. This analytical approach enabled the systematic quantification of pairwise correlations among the measured parameters, facilitating the identification of potential co-variation patterns between hydrological dynamics and water quality variations over the study period. Correlation coefficients between different forms of N form and other hydrological and water quality parameters are shown in Table 2. It should be noted that the concentration of N in different forms may be different along the direction of water flow, so the distance from the TGR was chosen to characterize the spatial difference. The results show the concentrations of NH 4 + -N and NO 3 − -N in the water exhibit a significant positive correlation with TN concentration, while NO 2 − -N shows an insignificant one. Substantial discrepancies exist in the correlations strength and nature of correlations between different N forms and other hydrological parameters. Comparison of correlations between different N forms and water quality parameters shows that NH 4 + -N had a significant positive correlation with the distance from the dam, while the other forms of N concentration had no significant correlation with the distance from the dam. Notably, all N forms are associated with the COD index: TN, NH 4 + -N, and NO 3 − -N exhibit significant negative correlations with COD , whereas NO 2 − -N shows a significant positive correlation with COD . Table 2 Correlation coefficient matrix between N concentration and other water quality indicators in water bodies EC 1.000 Note: *. Significant correlation at 0.05 level and **. Significant correlation at 0.01 level. BOD 5 1.000 -0.176 ORP 1.000 -0.244 * -0.304 ** COD 1.000 -0.299 * 0.181 0.011 DO 1.000 0.355 ** -0.175 0.146 -0.171 pH 1.000 0.056 0.110 -0.207 0.317 ** 0.013 T 1.000 -0.159 0.167 0.149 0.047 -0.096 -0.024 Q 1.000 − .264 * -0.064 -0.206 -0.183 -0.128 -0.131 0.404 ** Z 1.000 0.542 ** -0.168 0.098 0.010 0.080 − .385 ** -0.008 0.542 ** Distance 1.000 -0.878 ** -0.470 ** 0.201 -0.082 0.022 -0.120 0.503 ** -0.015 -0.604 ** TN 1.000 0.036 0.019 0.259 * -0.010 -0.323 ** -0.278 * − .0328 ** 0.305 ** -0.219 0.014 NO 2 − -N 1.000 0.054 0.111 -0.108 -0.225 0.049 -0.040 0.208 0.252 * -0.027 -0.069 -0.104 NO 3 − -N 1.000 -0.025 0.838 ** -0.065 0.098 0.370 ** -0.074 -0.294 * − .0357 ** -0.338 ** 0.155 -0.197 0.148 NH 4 + -N 1.000 0.152 0.287 * 0.314 ** 0.524 ** -0.425 ** -0.240 * 0.116 -0.095 0.013 -0.115 0.474 ** -0.002 -0.488 ** NH 4 + -N NO 3 − -N NO 2 − -N TN Distance Z Q T pH DO COD ORP BOD5 EC Meanwhile, the correlations between flow discharge and different N forms varied. NH 4 + -N exhibited a significant negative correlation with flow discharge ( p < 0.05) and a highly significant negative correlation with EC ( p < 0.01). In contrast, NO 3 − -N showed a highly significant positive correlation with flow discharge ( p < 0.01), and TN also had a significant positive correlation with flow discharge. The negative correlation between NO 2 − -N and flow discharge was not significant. Additionally, NH 4 + -N was significantly negatively correlated with water level and EC , but significantly positively correlated with ORP . Both TN and NO 3 − -N displayed significant negative correlations with pH and DO . Redundancy Analysis (RDA) is a widely used multivariate statistical technique in water quality studies, primarily employed to explore relationships between water quality variables and environmental factors. It can unravel associations between multiple response variables and multiple explanatory variables. RDA identifies which environmental factors significantly drive water quality variations and visualizes results via 2D or 3D ordination plots, enhancing the intuitiveness and interpretability of findings. The N concentrations and other hydrological and water quality parameters of the four stations from 1992 to 2000 were analyzed by RDA using Canoco 5 software, with TN, NO 3 − -N, NH 4 + -N and NO 2 − -N as response variables and other parameters as explanatory variables. The RDA ordination results illustrating the influence of other parameters on N speciation and concentration in the water body of the Jingjiang Reach are presented in Fig. 9 . The approximate correlation between response variables and explanatory variables was derived by projecting the arrows of response variables onto the lines of explanatory variable arrows. For example, NH 4 + -N concentration increased with the increasing distance from the dam, which was consistent with the results obtained from the previous correlation analysis. Meanwhile, the RDA results of COD (as an explanatory variable) and all response variables also indicated that COD exerted complex and profound impacts on river N concentrations in the Jingjiang Reach. According to Table 3 of the RDA forward selection results of other parameters for water N concentration, two parameters with p -value less than 0.05 were selected, COD and distance from the dam, which met the significance requirements, indicating that COD and distance from the dam were important influencing factors affecting N concentration in the water body, with the contribution rate of COD being 36.8% and the contribution rate of distance being 32.5%. This result is consistent with the correlation matrix analysis presented earlier. At the same time, it also shows that the TGR operation has a certain impact on the distribution of N in the water body of Jingjiang Reach. Table 3 RDA forward selection results of other parameters for water N concentration Parameter Interpretation rate(%) Contribution rate(%) F P value COD 19.0 36.8 8 0.002 Distance 16.8 32.5 8.6 0.002 4 Discussions 4.1 Potential reasons for variation of N distribution The study found that the construction and operation of the TGR have had a significant influence on the distribution and transformation of N. The results show that NH 4 + -N concentration increases with the distance from the TGR, which can be attributed to the altered flow regimes and sediment transport patterns in the river system post-dam. As demonstrated by the dramatic sediment retention in the TGR(Wenjie et al. 2022), which leads to a concomitant reduction in the delivery of sediment-associated nutrients, particularly organic nitrogen(Pang et al. 2022 ). This reduction in sediment transport leads to a decrease in N influx from the upstream regions, which might contribute to the observed lower concentrations of N further downstream. However, other N forms such as NO 3 − -N and NO 2 − -N showed no significant correlation with distance from the dam, indicating that the distribution of these N forms is governed by additional, more complex environmental factors. Beyond altering the physical conditions of flow and sediment to influence N distribution in the Jingjiang Reach, the TGR also indirectly modifies N dynamics by affecting key chemical indicators such as DO, COD, and BOD 5 . Furthermore, the dam's construction and operation inevitably impact the aquatic ecosystem(Li et al. 2022 ). The bio-environmental interactions subsequently lead to changes in water nutrient composition, indicating that the dam's influence on N distribution embodies a multi-faceted mechanism coupling physical, chemical, and biological processes. Furthermore, anthropogenic activities, as a non-negligible driver, interact with the aforementioned natural processes to jointly regulate the N cycle in the Jingjiang Reach. Against the backdrop of an altered hydrodynamic regime, the impacts of point-source discharges from domestic and industrial wastewater in riparian cities (e.g., Yichang, Jingzhou), as well as intensive agricultural non-point source pollution from the Jianghan Plain and Dongting Lake area, are amplified(Zhang et al. 2015 ;Zhang et al. 2023 ). Particularly noteworthy is that reduced flow velocities promote the sedimentation of fine-grained sediments and their adsorbed N pollutants(Kreiling et al. 2025 ). These nutrient-rich sediments can subsequently serve as a significant endogenous source, continuously releasing N nutrients into the overlying water body under disturbances such as temperature and pH fluctuations or ship navigation(Zhu et al. 2023 ; Rios-Yunes et al. 2023 ). Consequently, the distribution and transformation of N in the Jingjiang Reach essentially constitute a complex system co-governed by the synergistic effects of dam operation, natural processes, and anthropogenic discharges. Climatic factors, especially precipitation patterns and temperature fluctuations, significantly impact N cycling in the river system. Seasonal differences in hydrological regimes lead to seasonal variations of N concentrations in rivers(Gao et al. 2018 ). This phenomenon is likely attributed to the disparity in water discharge between the dry season and wet season: during the dry season, lower water discharge results in a higher ratio of pollutant discharge to runoff, rendering the water body more susceptible to pollution. Otherwise, temperature, particularly, influences the microbial processes of N mineralization and nitrification(Schaefer and Hollibaugh 2017 ). Warmer temperatures typically increase the rate of these processes, leading to higher concentrations of certain N forms, such as NH 4 + -N and NO 3 − -N. This seasonal variation further complicates the N dynamics and requires careful monitoring to predict the water quality changes across different timescales. 4.2 Implication and prospective This study reveals the impact of the TGR on N dynamics in the Jingjiang Reach, highlighting the need for comprehensive management of water quality and nutrient cycling, may provides vital insights for managing the N dynamics in the Jingjiang Reach, contributing to both environmental protection and the sustainable management of the Yangtze River. The observed increase in TN concentrations, especially at stations like Zhicheng and Shashi, indicates a growing risk of eutrophication and water pollution. This underscores the necessity of integrated N management strategies that account for the changes in hydrological regimes caused by the TGR, particularly the altered sediment transport and nutrient flux. The study also emphasizes the importance of long-term, multi-parameter monitoring to understand the seasonal and interannual fluctuations in N forms such as NH 4 + -N and NO 3 − -N. The significant influence of factors like flow discharge, water temperature, and ORP on N transformations should be incorporated into water quality management strategies to mitigate nutrient pollution. Future research should further investigate the mechanisms behind N form changes and their ecological impacts, particularly in response to altered hydrological and sediment dynamics. Modeling the interaction of natural and anthropogenic factors will help predict N concentrations under future scenarios. As climate change may influence precipitation patterns and temperatures, integrating these factors into N management will be crucial for sustainable river basin management and water quality protection in the Jingjiang Reach. 5 Conclusions This study analyzed the spatial and temporal distribution of N concentrations in the Jingjiang section of the Yangtze River, highlighting significant variations in N forms at both seasonal and interannual scales. Overall, N concentrations have shown an upward trend, with TN levels at Zhicheng and Shashi surpassing the Class IV water quality standard from 2013 to 2015, indicating an increasing pollution risk. Seasonal variations are notable, with TN and NO 3 − -N concentrations peaking during the normal water period (March-May), while NH 4 + -N concentrations are highest during the dry period (October-February). The TGR significantly affects N distribution in the Jingjiang Reach, altering the proportions of different N forms in the water. Key factors such as distance from the TGR and oxidation-reduction potential (ORP) play an important role in shaping N concentrations.The findings provide basic data and scientific support for comprehensive water quality management, and also lay a foundation for further quantitative analysis of the TGR’s impacts on the hydrological and water quality conditions of the Jingjiang Reach. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution **Yihao Wu:** Conceptualization, Methodology, Writing – original draft. **Yuhong Zeng:** Funding acquisition, Investigation, Conceptualization, Validation, Writing - review & editing. **Runpei Liu**: Data curation, Writing – original draft. **Xiaoning Liu**: Writing – review & editing, Validation. **Bao Qian:** Investigation, Validation. **Li Lin:** Writing - review & editing. Acknowledgement As I bring this work to completion, I would like to extend my warmest gratitude to my girlfriend, Boya Yang, for her constant support and companionship throughout my graduate studies. I am also deeply grateful to my family, whose endless encouragement has always been my driving force. My sincere appreciation goes to my colleagues in the laboratory, especially Xiaobing Meng and Chen Wang, for their valuable guidance and assistance. This work was financially supported by the National Natural Science Foundation of China ( No. 52320105006). 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Pang, Lina, Yanxin Sun, Yao Yue, et al. 2022. “Stability of Aquatic Nitrogen Cycle Under Dramatic Changes of Water and Sediment Inflows to the Three Gorges Reservoir.” GeoHealth 6 (8): e2022GH000607. Rios-Yunes, Dunia, Tim Grandjean, Alena di Primio, et al. 2023. “Sediment Resuspension Enhances Nutrient Exchange in Intertidal Mudflats.” Frontiers in Marine Science 10 (March). Schaefer, Sylvia C., and James T. Hollibaugh. 2017. “Temperature Decouples Ammonium and Nitrite Oxidation in Coastal Waters.” Research-article. ACS Publications, American Chemical Society, March 3. world. Wang, Sichu, Hongying Li, Xiaorong Wei, et al. 2020. “Dam Construction as an Important Anthropogenic Activity Disturbing Soil Organic Carbon in Affected Watersheds.” Environmental Science & Technology 54 (13): 7932–41. Wenjie, Li, Dai Zhuo, Xiao Yi, Yang Shengfa, and Yang Wei. 2022. “Influence of Sedimentation on Nitrogen and Phosphorus in the Three Gorges Reservoir.” International Journal of River Basin Management 20 (3): 311–21. Yang, Linhan, Sidong Zeng, Jun Xia, Yueling Wang, Renyong Huang, and Minghao Chen. 2022. “Effects of the Three Gorges Dam on the Downstream Streamflow Based on a Large-Scale Hydrological and Hydrodynamics Coupled Model.” Journal of Hydrology: Regional Studies 40 (April): 101039. Zhang, Jun, Rongfei Wei, Teklit Zerizghi, et al. 2023. “Nitrate Sources and Transformations along the Yangtze River and Its Changes after Strict Environmental Regulation.” Journal of Hydrology 617 (February): 129037. Zhang, W. S., D. P. Swaney, X. Y. Li, B. Hong, R. W. Howarth, and S. H. Ding. 2015. “Anthropogenic Point-Source and Non-Point-Source Nitrogen Inputs into Huai River Basin and Their Impacts on Riverine Ammonia–Nitrogen Flux.” Biogeosciences 12 (14): 4275–89. Zhu, Dantong, Xiangju Cheng, David J. Sample, Qingsong Qiao, and Zhaowei Liu. 2023. “Effect of Water Temperature on Internal Nitrogen Release from Sediments in the Pearl River Delta Region, China.” Hydrology Research 54 (9): 1055–71. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8410924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599901657,"identity":"392f9cc3-a4e3-4e4e-821e-db861b589d3e","order_by":0,"name":"Yihao Wu","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yihao","middleName":"","lastName":"Wu","suffix":""},{"id":599901658,"identity":"a71766c1-5f8e-4b3c-9e26-1433b12afeb2","order_by":1,"name":"Yuhong Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDACCTBpw2AAonhI0JJGupbDJGgxuN3+8HHBr/OJ2yUSGB+8bWOQNyeo5c6BZOOZfbcTd85IYDac28ZguLOBkJYbCcekeXtu5264kcAmzdvGkGBwgKCWxPbfvD3nQFrYfxOpJZmNmefHAbAtzERpkbyRxizN25Bcv+HMw2bJOeckDDcQ0sJ3I/3hZ54/dsYGx5MPfnhTZiNP0BYFkALGNhCTsYEBFk14gTxIHcMfwgpHwSgYBaNgBAMA2y5FaqsB3TAAAAAASUVORK5CYII=","orcid":"","institution":"Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Yuhong","middleName":"","lastName":"Zeng","suffix":""},{"id":599901659,"identity":"b5d8e6a9-5ace-44e1-bfea-a0fc8d567984","order_by":2,"name":"Runpei Liu","email":"","orcid":"","institution":"Changjiang River Scientific Research Institute, Yangtze Water Resources Commission","correspondingAuthor":false,"prefix":"","firstName":"Runpei","middleName":"","lastName":"Liu","suffix":""},{"id":599901660,"identity":"40ad62c5-6e4d-4b40-bcb0-e879503ffa40","order_by":3,"name":"Xiaoning Liu","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoning","middleName":"","lastName":"Liu","suffix":""},{"id":599901661,"identity":"67e6caed-ea97-45d0-804b-040dd6b8bd34","order_by":4,"name":"Bao Qian","email":"","orcid":"","institution":"Yangtze River Water Resources Commission, Bureau of Hydrology, Yangtze River Basin Water Quality Monitoring Center","correspondingAuthor":false,"prefix":"","firstName":"Bao","middleName":"","lastName":"Qian","suffix":""},{"id":599901662,"identity":"8c1596bd-f9e7-46fa-90b0-d67d4fc49cb4","order_by":5,"name":"Li Lin","email":"","orcid":"","institution":"Changjiang River Scientific Research Institute, Yangtze Water Resources Commission","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-12-20 09:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8410924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8410924/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104017664,"identity":"542c09bc-4643-4462-8365-57b40ff0aecf","added_by":"auto","created_at":"2026-03-05 17:31:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362487,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal trend of annual mean TN concentration in surface water in the Jingjiang Reach (1992–2015)\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/5a63f5b8837e30a1a187d418.jpg"},{"id":104017660,"identity":"583eeb05-4cb4-489c-a6a7-f1e37414a950","added_by":"auto","created_at":"2026-03-05 17:31:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111839,"visible":true,"origin":"","legend":"\u003cp\u003eThe multi-year-averaged proportion of different forms of N in the Jingjiang Reach from 1992 to 2015\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/ace8f4afa07bf37238e013f7.jpg"},{"id":104402860,"identity":"3cadedcb-eec0-4a4c-8748-9c50ccec6b96","added_by":"auto","created_at":"2026-03-11 12:16:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":413383,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of TN concentration over time in the Jingjiang Reach (1992-2022)\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/2bb5083775acfb9bec4f3cb3.jpg"},{"id":104403196,"identity":"b3156db2-e426-4be0-a160-f36207df9f11","added_by":"auto","created_at":"2026-03-11 12:17:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":456861,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of annual mean NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N over time in the Jingjiang Reach(1992-2022)\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/cfa4dc89ae55dbfadf1326ff.jpg"},{"id":104017661,"identity":"6638f5d8-e287-4786-8502-f03e1060db31","added_by":"auto","created_at":"2026-03-05 17:31:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":414147,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal distribution of N concentration in Jingjiang Reach\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/4ae9d512fb44d6d10ff1930e.jpg"},{"id":104017669,"identity":"0e09ed19-81f3-4873-abfb-7755f64316a3","added_by":"auto","created_at":"2026-03-05 17:31:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":453908,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of N patterns in the three impoundment stages of the Jingjiang Reach\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/4f7bbfede7693f5330027414.jpg"},{"id":104017666,"identity":"07642af3-9ffc-4321-a86e-b9180158da49","added_by":"auto","created_at":"2026-03-05 17:31:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":312345,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Variations of water Temperature, water Level, and discharge in the Jingjiang Reach (1992-2000)\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/bb45f31631c6057296b5ceb1.jpg"},{"id":104402134,"identity":"d5da254a-6240-41c1-8b4b-fede34e30d5c","added_by":"auto","created_at":"2026-03-11 12:14:26","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":450143,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Variations of EC, pH, ORP, and oxygen-related parameters in the Jingjiang Reach (1992-2000)\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/b6523ed9532f1c6534a3abde.jpg"},{"id":104017667,"identity":"4584392c-0f21-482b-b56c-72c2b9a606b1","added_by":"auto","created_at":"2026-03-05 17:31:42","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":27304,"visible":true,"origin":"","legend":"\u003cp\u003eRDA ranking diagram of the effects of other parameters on N concentrations in the Jingjiang Reach\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/8d0a41d68d64c2238337f484.jpg"},{"id":104408663,"identity":"c40b6dab-652a-42e2-8423-c72e3abc2901","added_by":"auto","created_at":"2026-03-11 12:43:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3995843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8410924/v1/98e69e73-6fe7-4b5f-9dcc-a80da58cfe47.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial-temporal distribution characteristics of nitrogen in the Jingjiang reach of the Yangtze River affected by the operation of Three Gorges Reservoir","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAs one of the world\u0026rsquo;s largest hydroelectric and water conservancy projects, the construction and operation of the TGR have exerted profound and multidimensional impacts on the hydrological regime and water quality in the lower reaches of the Yangtze River, with particularly prominent effects on the biogeochemical cycling of nutrients, most notably Nitrogen(N) cycling dynamics (Wang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al. 2021). The TGR\u0026rsquo;s regulation of the Yangtze River directly alters key hydrological processes in its downstream reaches, such as the natural flow regime, as well as the spatiotemporal distribution of nutrients like nitrogen (Yang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although the TGR delivers substantial socioeconomic benefits, its unintended impacts on N cycling and downstream water quality pose significant challenges to integrated river basin management (Nie et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWater and sediment are the main carriers of nutrients (Wijesiri et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The change of discharge-sediment flux relationship caused by the operations of hydraulic engineering also has an important impact on the flux of nutrients (Gong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specially, the operation of reservoirs can significantly affect river runoff and sediment transport processes (Wang and Wang, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which in turn will also have an impact on the water environment of downstream rivers (Andualem et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hamidifar et al., 2024). It has been proved that the construction and operation of dams can alter the seasonal characteristics of N flux and transformation in river networks (Yang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gan et al., 2025). The migration and transformation of N, as well as its chemo-biological reaction processes, are susceptible to various environmental factors (He et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which leads to that the morphological transformation and distribution of N are complex and unpredictable (Wang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gan et al., 2025). Most existing studies on N concentrations and other hydrological quality indices in the Jingjiang Reach have been conducted within a relatively short monitoring period. There remains a lack of in-depth analysis on various water body indices in this reach over an extended time span.\u003c/p\u003e \u003cp\u003ePrevious studies showed that the temporal and spatial distribution characteristics and change trends of hydrological and water quality indexes in the Jingjiang River reach may lead to changes in the forms of N and affect the water quality conditions (He et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, seasonal differences in hydrological conditions can lead to seasonal variations in N concentrations in river waters (Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, previous studies mostly focused on the temporal changes of a single environmental variable, but not enough on the spatial changes especially under the effect of the regulation of the TGR.\u003c/p\u003e \u003cp\u003eWater pollution associated with rapid economic development along large rivers has received considerable attention in which eutrophication is one of the greatest threats to surface water worldwide (Huang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gao, 2024). As an essential component of living organisms and the main nutrients, N is closely related to the pollution conditions and trophic status of the water bodies (Jiang and Nakano, 2022; Kakade et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the gradual urbanization in the Yangtze River Basin, the N content in the Jingjiang Reach of the middle stream of Yangtze river has an increasing trend (Cui et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There is a lack of systematic research on the distribution characteristics of N forms and N concentrations in the water body of the Jingjiang Reach.\u003c/p\u003e \u003cp\u003eRegarding the gaps of previous studies, this paper analyzed the distribution characteristics of various forms of N in the water body of Jingjiang Reach, discussed their relationships with hydrological and other water quality indicators based on a long term hydrological and water quality data collected from four hydrological stations set along the Jingjiang Reach. Lastly, the effects of the TGR were comparatively analyzed and clarified. It is anticipated that the results will provide a new understanding on the environment effect of TGR operation.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research area\u003c/h2\u003e \u003cp\u003eThe Jingjiang Reach meanders through the flat Jianghan Plain of Hubei Province, China, which is an important part of the waterway of the main stream of the Yangtze River. With an average annual rainfall ranging from about 1000 to 1500 millimeters, and an average annual temperature around 16 to 18 degrees Celsius, the Jingjiang Reach has received extensive attention due to its ecological value, since it involves four historically recorded spawning grounds for the \"Four Major Chinese Carps\", and seven nature reserves, for Yangtze finless porpoise and other important and commercial fish species.\u003c/p\u003e \u003cp\u003eThe Jingjiang Reach (from Zhijiang to Chenglingji), located downstream of the TGR, is significantly affected by the operation of reservoir in terms of its hydrological rhythm. Generally, the impoundment of the TGR is divided into three stages (S-Ⅰ, S-Ⅱ and S-Ⅲ) according the storage level, with corresponding occurrence time in 2003 (135 m), 2006 (156 m) and 2010 (175 m) respectively. During the operation of the TGR, different dispatching strategies are adopted according to different water periods: during the post-flood storage period (October - December), the water level is stored to the normal storage level of 175 meters; in the dry season (January - May), water is gradually released to meet the needs of the lower reaches; during the wet season (June - September), a relatively low water level of about 145 meters (flood control limited water level) is maintained to reserve storage capacity for flood control.\u003c/p\u003e \u003cp\u003eThe operation of the TGR has significantly altered the sediment transport and flow discharge of the Jingjiang Reach. From 1990 to 2021, the annual mean runoff of the Jingjiang Reach remained stable over the long term, ranging between 350 and 400\u0026nbsp;billion cubic meters (km\u0026sup3;). From an intra-annual perspective, the proportion of runoff occurring during the flood season decreased from 70% in the period 1990\u0026ndash;2002 to 65% in 2003\u0026ndash;2021. The sediment concentration in the Jingjiang Reach exhibited a \"stepwise decreasing\" trend. Taking 1992\u0026ndash;2002 as the pre-impoundment period (baseline), the sediment concentration decreased by 53.4% during the first-stage impoundment (2003\u0026ndash;2008), by 85.5% during the second-stage impoundment (2009\u0026ndash;2012) and by 90.4% during the third-stage impoundment (2013\u0026ndash;2021). These results demonstrate the significant sediment-trapping effect of the TGR (Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources\u003c/h2\u003e \u003cp\u003eFour national hydrological stations in the middle stream of Yangtze River, namely Yichang, Zhicheng, Shashi, and Chenglingji, which are 42.5 km, 90 km, 187 km, and 420 km away from the TGR respectively were selected. Among them, the Yichang station is the outlet station of the TGR and serves as an important station connecting the upstream reservoir and the downstream Jingjiang Reach; the Zhicheng station is the starting point of the Jingjiang River section; and the Chenglingji station is the endpoint of the Jingjiang River section.\u003c/p\u003e \u003cp\u003eLong-term data on N forms concentrations (1992\u0026ndash;2015) were collected from hydrological bureaus and water quality monitoring platforms at four stations (Yichang, Zhicheng, Shashi, and Chenglingji). These data included concentrations of nitrite nitrogen (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, mg/L), nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, mg/L), ammonium nitrogen (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, mg/L), and total nitrogen (TN, mg/L). Given that the TGR underwent three stages of impoundment in 2003, 2006, and 2008\u0026ndash;2010, respectively, the 1992\u0026ndash;2015 dataset effectively captures the impacts of TGR construction and impoundment processes on N concentration variations in the Jingjiang Reach.\u003c/p\u003e \u003cp\u003eAdditionally, other hydrological and water quality parameters (1992\u0026ndash;2000) were collected for each station, including water level (\u003cem\u003eZ\u003c/em\u003e, m), discharge (\u003cem\u003eQ\u003c/em\u003e, m\u0026sup3;/s), water temperature (\u003cem\u003eT\u003c/em\u003e, ℃), pH, electrical conductivity (\u003cem\u003eEC\u003c/em\u003e, \u0026micro;S/cm), oxidation-reduction potential (\u003cem\u003eORP\u003c/em\u003e, mV), and dissolved oxygen (\u003cem\u003eDO\u003c/em\u003e, mg/L).This comprehensive dataset provides a robust basis for analyzing spatiotemporal patterns of N dynamics and their responses to hydrological modifications induced by the TGR.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial distribution of N concentrations in water\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the longitudinal variation trend of annual mean TN concentrations along the Jingjiang Reach from 1992 to 2015. At all the monitoring stations, TN consistently exceeds the value for class Ⅱ (0.5 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of the Chinese guideline (GB3838 2002), notably, the TN concentrations at Zhicheng station and Shashi station have consistently exceeded the allowed limit (class Ⅲ, 1.0 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) across all observed years. Within the observation period, some monitoring values at all four stations met the national Class IV surface-water threshold (1.5 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Notably, the water quality data of the Zhicheng and Shashi stations from 2013 to 2015 even reached the national Class V surface-water threshold (2.0 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), the highest (most polluted) class in the national surface water quality classification system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn analysis of the multi-year average TN concentration reveals a distinct trend along the flow direction: it generally increases initially and then decreases. Specifically, the TN concentration starts at 1.378 mg/L at the Yichang station, rises to 1.665 mg/L at the Zhicheng station, and finally declines to 1.110 mg/L at the Chenglingji station. Relative to the Yichang station, the TN concentration at the Chenglingji station shows a net decrease of 19.4%. Notably, there is no significant difference in TN concentration between the Shashi station and the Zhicheng station.\u003c/p\u003e \u003cp\u003eThe spatial distribution of the proportion of N in various forms is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The proportion of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N at the four stations is all lower than 5%. The proportion of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N gradually increased from 15% in Yichang station to 31% in Chenglingji station, while the proportion of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N decreased from 82% in Yichang station to 66% in Chenglingji station. No obvious spatial variation trend was observed in the proportion of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N among the four monitoring sites, which may be associated with the generally low concentration of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe proportion of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N increased along the reach, while that of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N decreased longitudinally. Two potential mechanisms underlying this pattern are proposed as follows: first, the annual mean water temperature at the four monitoring stations ranged approximately from 15 ℃ to 20 ℃. Within this temperature range, the activity of ammonifying bacteria is relatively high, whereas the activity of nitrifying bacteria remains low. This imbalance between ammonification and nitrification can lead to the accumulation of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and a relative reduction in NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N along the reach. Second, increasing anthropogenic disturbances along the Jingjiang Reach may have directly elevated the input of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N into the water body, thereby increasing its proportion relative to other N forms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal distribution of N concentrations in water\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Interannual distribution characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the growth trend of TN for each station is obvious from 1992 to 2003, and the variation curve is relatively flat from 2003 to 2009. Considering that 2003 is the time node of the first stage of the water storage of TGR, it is indicating that the dam construction has a certain impact on the N distribution in the water body of the Jingjiang Reach. At the same time, the TN concentration of Chenglingji station and Yichang station decreased slightly from 2015 to 2021, while the TN concentration of Zhicheng station and Shashi station have no obvious change. According to Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the concentration of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N in the Jingjiang Reach fluctuates with an overall decreasing trend.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOver an extended time span, the annual mean N concentration in the Jingjiang Reach has increased year by year, with the issue of N pollution worsening gradually. Notably, taking 2003 as a temporal node, the increasing trend of annual mean TN concentration slowed down, while the variation trend of annual mean NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentration shifted. It indicates that the TGR may exert a certain impact on the N distribution in the water body of the Jingjiang Reach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Seasonal distribution characteristics\u003c/h2\u003e \u003cp\u003eThe N concentration in the Jingjiang Reach also showed certain seasonal differences. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, except for Chenglingji station, the TN concentration of the other three stations was the highest in the normal water period (from March to May), and the seasonal distribution trend of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N was similar to that of TN. The NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentrations in Zhicheng, Shashi and Chenglingji stations were the highest during the dry season (from October to February), which was affected by seasonal factors to a certain extent. Significance test by SPSS shows that this change was not obvious (the \u003cem\u003ep\u003c/em\u003e value was larger than 0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Distribution characteristics of different water impoundment stages\u003c/h2\u003e \u003cp\u003eSingle-factor Analysis of Variance (ANOVA) is a statistical tool employed to determine whether the differences in mean values among three or more groups are statistically significant. In this study, single-factor ANOVA was applied to analyze two sets of key affecting parameters: one is the seasonal variations of environmental or hydrological parameters in the Jingjiang Reach of the Yangtze River; and the other is the variations of these parameters across different impoundment stages of the TGR. Through this analytical approach, the statistical significance of changes in the target parameters was tested, providing a quantitative basis for verifying the magnitude of parameter fluctuations induced by seasonal dynamics and reservoir operation.\u003c/p\u003e \u003cp\u003eThe distribution characteristics of different forms of N in the water bodies of each station in the Jingjiang Reach during different impoundment stages of the TGR are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results of the one-way analysis of variance for the distribution of N concentrations at different stages are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It was found that the three-stage impoundment process of the TGR exerted a significant impact on the variation trends of TN, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentrations at the Yichang and Zhicheng stations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas its impact on the variation trend of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentration was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, the concentrations of TN, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N at the Shashi station were all significantly affected by the TGR impoundment to a certain extent. At the Chenglingji station, the concentrations of TN and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N in the water body were also affected by the reservoir impoundment, with a relatively significant degree of influence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way analysis of variance of N concentration distribution characteristics at different water storage stages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage changes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYichang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTN、NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N、NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhicheng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N、TN、NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShashi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTN、NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N、NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChenglingji\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N、TN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe concentrations of TN and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N in the water body of Chenglingji station are also significantly affected by the water storage of TGR(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The TN concentration at Chenglingji station increased from 1.5 mg/L in the first stage to 1.848 mg/L in the third stage, with an overall increase of 23%. The TN concentration of the other stations also continued to increase from the first to the third stage. The NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentration at Yichang station decreased from 0.12 mg/L in the first stage to 0.078 mg/L in the third stage. These results indicate that the impoundment of water in the TGR affects the distribution characteristics of N in the water body of the Jingjiang Reach and the proportion of N in various forms to a certain extent. At the same time, it was also found that NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N is less affected by the water storage in the reservoir area. On the one hand, it is due to the unstable state of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N in the water body, which is easy to convert into other forms of N. On the other hand, it may be due to the low concentration of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N in the water body of the Jingjiang Reach, and some of the data are lower than the detecting limit during the actual measurement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution characteristics of N concentrations across different impoundment stages of the TGR revealed that the operation of the TGR has altered the hydrological regime of the downstream Jingjiang Reach. Concurrently, it has modified the spatiotemporal distribution of various N forms in the river and may have effect on the fluvial ecosystem\u0026mdash;exerting significant impacts on N transport and transformation processes. Therefore, in general, the effects of the TGR\u0026rsquo;s staged impoundment mode on N concentrations and N forms require special attention in river basin management and ecological protection practices.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effect of Three Gorges Reservoir on N distribution in Jingjiang Reach\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Variations of Hydrological and Water Quality Parameters\u003c/h2\u003e \u003cp\u003eThe transformation and transport of N in aquatic systems are governed by a complex interplay of physical, chemical, and biological processes. Furthermore, the operation of the TGR has exerted a considerable influence on various hydrological and water quality parameters. Therefore, a detailed investigation into the variations of these parameters is essential. Such an analysis is crucial for elucidating the distribution patterns of N concentrations and holds significant implications for subsequent research.\u003c/p\u003e \u003cp\u003eThe Jingjiang Reach, spanning approximately 360 km, exhibits a substantial spatial gradient from upstream to downstream. This is reflected in pronounced spatial disparities in water level, discharge, water temperature, EC, pH, ORP, DO, COD, and BOD\u003csub\u003e5\u003c/sub\u003e at monitoring stations along the reach. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, from 1992 to 2000, the multi-year average water level was highest at the Yichang station and gradually decreased along the flow direction, reaching 24.70 m at the Chenglingji station. The spatial distribution of discharge followed a similar pattern, with multi-year averages of 13,270.93 m\u003csup\u003e3\u003c/sup\u003e/s (Yichang), 13,408.45 m\u003csup\u003e3\u003c/sup\u003e/s (Zhicheng), 12,632.29 m\u003csup\u003e3\u003c/sup\u003e/s (Shashi), and 6,815.95 m\u003csup\u003e3\u003c/sup\u003e/s (Chenglingji). The multi-year average water temperature exhibited an initial decrease followed by an increase along the river course, although the overall trend was not pronounced.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the averaged EC, pH, ORP, DO, COD, and BOD\u003csub\u003e5\u003c/sub\u003e from 1992 to 2000 of the four sites have been illustrated. In contrast, the average EC demonstrated a clear decreasing trend from 339.9 \u0026micro;S/cm at Yichang to 265.3 \u0026micro;S/cm at Chenglingji, with a minimal difference observed between Zhicheng and Shashi. The average ORP at Yichang was relatively low, differing significantly from the other three stations. ORP generally increased downstream, suggesting an enhancement in the oxidizing capacity of the water body along the Jingjiang Reach.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe multi-year average DO concentration decreased from 8.82 mg/L at Yichang to 8.50 mg/L and 8.51 mg/L at Zhicheng and Shashi, respectively, before increasing to 8.81 mg/L at Chenglingji. The spatial distributions of COD and BOD\u003csub\u003e5\u003c/sub\u003e concentrations were similar to that of DO, all displaying a pattern of higher values at the terminal stations and lower values in the middle section, albeit with a less pronounced trend for BOD\u003csub\u003e5\u003c/sub\u003e. Overall, the multi-year average DO concentrations along the Jingjiang Reach met the Class I water quality standard (\u0026gt;\u0026thinsp;7.5 mg/L), as did the COD and BOD\u003csub\u003e5\u003c/sub\u003e levels. The tri-oxygen parameters (COD, BOD\u003csub\u003e5\u003c/sub\u003e, DO) demonstrate that during the three construction and operation phases of the TGR (1992\u0026ndash;2000), organic pollution in the Jingjiang Reach decreased, and water quality improved to some extent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Correlation analysis\u003c/h2\u003e \u003cp\u003eKendall's τ correlation coefficient is a non-parametric statistical metric employed to quantify the nonlinear relationship between two variables. In this section, Kendall's τ correlation analysis was applied to long-term time-series monitoring data of multiple hydrological and water quality parameters. This analytical approach enabled the systematic quantification of pairwise correlations among the measured parameters, facilitating the identification of potential co-variation patterns between hydrological dynamics and water quality variations over the study period.\u003c/p\u003e \u003cp\u003eCorrelation coefficients between different forms of N form and other hydrological and water quality parameters are shown in Table\u0026nbsp;2. It should be noted that the concentration of N in different forms may be different along the direction of water flow, so the distance from the TGR was chosen to characterize the spatial difference.\u003c/p\u003e \u003cp\u003eThe results show the concentrations of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N in the water exhibit a significant positive correlation with TN concentration, while NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N shows an insignificant one. Substantial discrepancies exist in the correlations strength and nature of correlations between different N forms and other hydrological parameters.\u003c/p\u003e \u003cp\u003eComparison of correlations between different N forms and water quality parameters shows that NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N had a significant positive correlation with the distance from the dam, while the other forms of N concentration had no significant correlation with the distance from the dam. Notably, all N forms are associated with the \u003cem\u003eCOD\u003c/em\u003e index: TN, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N exhibit significant negative correlations with \u003cem\u003eCOD\u003c/em\u003e, whereas NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N shows a significant positive correlation with \u003cem\u003eCOD\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eTable\u0026nbsp;2 Correlation coefficient matrix between N concentration and other water quality indicators in water bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eNote: *. Significant correlation at 0.05 level and **. Significant correlation at 0.01 level.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBOD\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eORP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.244\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.304\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCOD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.299\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.355\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.317\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.264\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.404\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.542\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.385\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.542\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.878\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.470\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.503\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.604\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.259\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.323\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.278\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0328\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.305\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.252\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.370\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.294\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0357\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.338\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.314\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.524\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.425\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.240\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.474\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.488\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eDO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eCOD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eORP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eBOD5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003eEC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, the correlations between flow discharge and different N forms varied. NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N exhibited a significant negative correlation with flow discharge (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a highly significant negative correlation with \u003cem\u003eEC\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N showed a highly significant positive correlation with flow discharge (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and TN also had a significant positive correlation with flow discharge. The negative correlation between NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and flow discharge was not significant. Additionally, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N was significantly negatively correlated with water level and \u003cem\u003eEC\u003c/em\u003e, but significantly positively correlated with \u003cem\u003eORP\u003c/em\u003e. Both TN and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N displayed significant negative correlations with pH and \u003cem\u003eDO\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eRedundancy Analysis (RDA) is a widely used multivariate statistical technique in water quality studies, primarily employed to explore relationships between water quality variables and environmental factors. It can unravel associations between multiple response variables and multiple explanatory variables. RDA identifies which environmental factors significantly drive water quality variations and visualizes results via 2D or 3D ordination plots, enhancing the intuitiveness and interpretability of findings.\u003c/p\u003e \u003cp\u003eThe N concentrations and other hydrological and water quality parameters of the four stations from 1992 to 2000 were analyzed by RDA using Canoco 5 software, with TN, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N as response variables and other parameters as explanatory variables. The RDA ordination results illustrating the influence of other parameters on N speciation and concentration in the water body of the Jingjiang Reach are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe approximate correlation between response variables and explanatory variables was derived by projecting the arrows of response variables onto the lines of explanatory variable arrows. For example, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentration increased with the increasing distance from the dam, which was consistent with the results obtained from the previous correlation analysis. Meanwhile, the RDA results of \u003cem\u003eCOD\u003c/em\u003e (as an explanatory variable) and all response variables also indicated that \u003cem\u003eCOD\u003c/em\u003e exerted complex and profound impacts on river N concentrations in the Jingjiang Reach.\u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e of the RDA forward selection results of other parameters for water N concentration, two parameters with \u003cem\u003ep\u003c/em\u003e-value less than 0.05 were selected, \u003cem\u003eCOD\u003c/em\u003e and distance from the dam, which met the significance requirements, indicating that \u003cem\u003eCOD\u003c/em\u003e and distance from the dam were important influencing factors affecting N concentration in the water body, with the contribution rate of \u003cem\u003eCOD\u003c/em\u003e being 36.8% and the contribution rate of distance being 32.5%. This result is consistent with the correlation matrix analysis presented earlier. At the same time, it also shows that the TGR operation has a certain impact on the distribution of N in the water body of Jingjiang Reach.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRDA forward selection results of other parameters for water N concentration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterpretation rate(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContribution rate(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Potential reasons for variation of N distribution\u003c/h2\u003e \u003cp\u003eThe study found that the construction and operation of the TGR have had a significant influence on the distribution and transformation of N. The results show that NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentration increases with the distance from the TGR, which can be attributed to the altered flow regimes and sediment transport patterns in the river system post-dam. As demonstrated by the dramatic sediment retention in the TGR(Wenjie et al. 2022), which leads to a concomitant reduction in the delivery of sediment-associated nutrients, particularly organic nitrogen(Pang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This reduction in sediment transport leads to a decrease in N influx from the upstream regions, which might contribute to the observed lower concentrations of N further downstream. However, other N forms such as NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N showed no significant correlation with distance from the dam, indicating that the distribution of these N forms is governed by additional, more complex environmental factors.\u003c/p\u003e \u003cp\u003eBeyond altering the physical conditions of flow and sediment to influence N distribution in the Jingjiang Reach, the TGR also indirectly modifies N dynamics by affecting key chemical indicators such as DO, COD, and BOD\u003csub\u003e5\u003c/sub\u003e. Furthermore, the dam's construction and operation inevitably impact the aquatic ecosystem(Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The bio-environmental interactions subsequently lead to changes in water nutrient composition, indicating that the dam's influence on N distribution embodies a multi-faceted mechanism coupling physical, chemical, and biological processes.\u003c/p\u003e \u003cp\u003eFurthermore, anthropogenic activities, as a non-negligible driver, interact with the aforementioned natural processes to jointly regulate the N cycle in the Jingjiang Reach. Against the backdrop of an altered hydrodynamic regime, the impacts of point-source discharges from domestic and industrial wastewater in riparian cities (e.g., Yichang, Jingzhou), as well as intensive agricultural non-point source pollution from the Jianghan Plain and Dongting Lake area, are amplified(Zhang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e;Zhang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Particularly noteworthy is that reduced flow velocities promote the sedimentation of fine-grained sediments and their adsorbed N pollutants(Kreiling et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These nutrient-rich sediments can subsequently serve as a significant endogenous source, continuously releasing N nutrients into the overlying water body under disturbances such as temperature and pH fluctuations or ship navigation(Zhu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rios-Yunes et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, the distribution and transformation of N in the Jingjiang Reach essentially constitute a complex system co-governed by the synergistic effects of dam operation, natural processes, and anthropogenic discharges.\u003c/p\u003e \u003cp\u003eClimatic factors, especially precipitation patterns and temperature fluctuations, significantly impact N cycling in the river system. Seasonal differences in hydrological regimes lead to seasonal variations of N concentrations in rivers(Gao et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This phenomenon is likely attributed to the disparity in water discharge between the dry season and wet season: during the dry season, lower water discharge results in a higher ratio of pollutant discharge to runoff, rendering the water body more susceptible to pollution. Otherwise, temperature, particularly, influences the microbial processes of N mineralization and nitrification(Schaefer and Hollibaugh \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Warmer temperatures typically increase the rate of these processes, leading to higher concentrations of certain N forms, such as NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N. This seasonal variation further complicates the N dynamics and requires careful monitoring to predict the water quality changes across different timescales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Implication and prospective\u003c/h2\u003e \u003cp\u003eThis study reveals the impact of the TGR on N dynamics in the Jingjiang Reach, highlighting the need for comprehensive management of water quality and nutrient cycling, may provides vital insights for managing the N dynamics in the Jingjiang Reach, contributing to both environmental protection and the sustainable management of the Yangtze River. The observed increase in TN concentrations, especially at stations like Zhicheng and Shashi, indicates a growing risk of eutrophication and water pollution. This underscores the necessity of integrated N management strategies that account for the changes in hydrological regimes caused by the TGR, particularly the altered sediment transport and nutrient flux.\u003c/p\u003e \u003cp\u003eThe study also emphasizes the importance of long-term, multi-parameter monitoring to understand the seasonal and interannual fluctuations in N forms such as NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N. The significant influence of factors like flow discharge, water temperature, and \u003cem\u003eORP\u003c/em\u003e on N transformations should be incorporated into water quality management strategies to mitigate nutrient pollution.\u003c/p\u003e \u003cp\u003eFuture research should further investigate the mechanisms behind N form changes and their ecological impacts, particularly in response to altered hydrological and sediment dynamics. Modeling the interaction of natural and anthropogenic factors will help predict N concentrations under future scenarios. As climate change may influence precipitation patterns and temperatures, integrating these factors into N management will be crucial for sustainable river basin management and water quality protection in the Jingjiang Reach.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study analyzed the spatial and temporal distribution of N concentrations in the Jingjiang section of the Yangtze River, highlighting significant variations in N forms at both seasonal and interannual scales. Overall, N concentrations have shown an upward trend, with TN levels at Zhicheng and Shashi surpassing the Class IV water quality standard from 2013 to 2015, indicating an increasing pollution risk. Seasonal variations are notable, with TN and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentrations peaking during the normal water period (March-May), while NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentrations are highest during the dry period (October-February).\u003c/p\u003e \u003cp\u003eThe TGR significantly affects N distribution in the Jingjiang Reach, altering the proportions of different N forms in the water. Key factors such as distance from the TGR and oxidation-reduction potential (ORP) play an important role in shaping N concentrations.The findings provide basic data and scientific support for comprehensive water quality management, and also lay a foundation for further quantitative analysis of the TGR\u0026rsquo;s impacts on the hydrological and water quality conditions of the Jingjiang Reach.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**Yihao Wu:** Conceptualization, Methodology, Writing \u0026ndash; original draft. **Yuhong Zeng:** Funding acquisition, Investigation, Conceptualization, Validation, Writing - review \u0026amp; editing. **Runpei Liu**: Data curation, Writing \u0026ndash; original draft. **Xiaoning Liu**: Writing \u0026ndash; review \u0026amp; editing, Validation. **Bao Qian:** Investigation, Validation. **Li Lin:** Writing - review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAs I bring this work to completion, I would like to extend my warmest gratitude to my girlfriend, Boya Yang, for her constant support and companionship throughout my graduate studies. I am also deeply grateful to my family, whose endless encouragement has always been my driving force. My sincere appreciation goes to my colleagues in the laboratory, especially Xiaobing Meng and Chen Wang, for their valuable guidance and assistance. This work was financially supported by the National Natural Science Foundation of China ( No. 52320105006).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndualem, Tesfa Gebrie, Guna A. Hewa, Baden R. Myers, Stefan Peters, and John Boland. 2023. \u0026ldquo;Erosion and Sediment Transport Modeling: A Systematic Review.\u0026rdquo; \u003cem\u003eLand\u003c/em\u003e 12 (7): 1396.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamidifar, Hossein, Michael Nones, and Pawel M. 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Sample, Qingsong Qiao, and Zhaowei Liu. 2023. \u0026ldquo;Effect of Water Temperature on Internal Nitrogen Release from Sediments in the Pearl River Delta Region, China.\u0026rdquo; Hydrology Research 54 (9): 1055\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Jingjiang Reach of the Yangtze River, temporal and spatial distribution of nitrogen, water quality, Three Gorges Reservoir","lastPublishedDoi":"10.21203/rs.3.rs-8410924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8410924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe regulation of water storage in the Three Gorges Reservoir (TGR) has altered the migration and transformation of nutrients in the middle and lower reaches of the Yangtze River. In this study, long-term hydrological and water quality monitoring data (before and after the operation of the TGR) for Yichang, Zhicheng, Shashi, and Chenglingji along the Jingjiang Reach in the middle-stream of Yangtze River were analyzed, and the distribution characteristics of nitrogen (N) in overlying water were quantified. The results shows that the annual average total nitrogen (TN) concentration in the Jingjiang Reach increased from 1992 to 2015, while the ammonium nitrogen (NH4+-N) concentrations showed a downward trend. The variation trends of both N forms shifted around 2003 when TGR begins to storage water. Seasonal variations were observed, with the highest concentrations of TN and nitrate nitrogen (NO3\u0026ndash;N) occurring during the normal water period (March-May). During this period, the average TN concentration across the four monitoring sites was 33% and 19.8% higher than those during the dry and wet periods, respectively. In contrast, the peak concentrations of NH4+-N was observed during the dry period (October-February). Additionally, correlation analysis and Redundancy Analysis (RDA) revealed that Chemical Oxygen Demand (COD) and the distance from the TGR were the most influential factors on N concentrations in the Jingjiang Reach, with corresponding contribution rates of 36.8% and 32.5%, and the distance from TGR exerted a significant positive effect on NH4+-N concentration, while COD showed a significant negative effect on TN concentration. The results suggested that the impoundment of the TGR has had a significant impact on the N content in the Jingjiang Reach, providing a theoretical support for nutrient management in the Yangtze River.\u003c/p\u003e","manuscriptTitle":"Spatial-temporal distribution characteristics of nitrogen in the Jingjiang reach of the Yangtze River affected by the operation of Three Gorges Reservoir","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 17:31:33","doi":"10.21203/rs.3.rs-8410924/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bad6aa6a-e568-4d0b-a9ff-6a9ea22bdcd8","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T07:10:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 17:31:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8410924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8410924","identity":"rs-8410924","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}