Introduction

The emergence of issues such as global population growth, food crises, and climate change in recent decades has meant that the spatiotemporal imbalance between water supply and demand has become increasingly severe, with regional water scarcity increasingly prominent1,2,3,4,5. To redistribute the water resources and alleviate regional water scarcity, many countries have constructed cross-basin water diversion projects6,7,8,9, with the over 160 large-scale cross-basin water diversion projects worldwide, including the California North-to-South Water Transfer Project in the USA10, the National River Linking Project in India11, and the Middle and Eastern Routes of the South to North Water Diversion Project in China12. The water diverted through these projects is typically designated for human consumption; thus, the requirements for the water environment in cross-basin water diversion projects are generally stringent13. Unlike natural water bodies, the flow processes in cross-basin water diversion projects are highly regulated. The water environment characteristics of cross-basin water diversion projects frequently exhibit substantial differences compared to those of natural water bodies14,15. Therefore, it is crucial to conduct a thorough evaluation and analysis of the water environment status in cross-basin water diversion projects to ensure the safety of the water supply14.

Conventional water quality parameters and Chl-a are two important indicators of the water environment status16,17,18,19,20. Various methods that have been used for water quality assessment include the WQI and single-factor evaluation methods21,22,23,24,25. The WQI method demonstrates the capacity to comprehensively leverage data on water quality parameters and transforms a variety of parameters into a single numerical value, which is extensively utilized in water quality assessment26,27,28,29,30. Chl-a, as a key component of algal cells, serves as a valuable indicator for evaluating the water environment status by reflecting the biomass of planktonic algae31,32,33.

The MR of the SNWDPC, which connects the Yangtze River with the Yellow River and Haihe River basins in China, is one of the most important cross-basin water diversion projects globally7,13. The project is aimed at alleviating severe water shortages in North China, optimizing water resource allocation, ensuring safe drinking water34,35,36. Operation of the MR in the SNWDPC began on December 12, 2014, and the cumulative diverted water volume has exceeded 67 billion m3 as of December 2023, directly benefiting over 176 million people. The water from the project is primarily used for domestic, industrial, and agricultural purposes, and must therefore meet the Class II standards in the Environmental Quality Standards for Surface Water37. Therefore, protecting the water environment is of utmost importance, and understanding the spatiotemporal variations that affect the water environment is crucial. However, the hardened concrete channels that comprise the MR of the SNWDPC do not intersect with adjacent rivers; thus, the project is a completely enclosed artificial system and the water characteristics differ from those of natural water bodies. The water environment status of the MR of the SNWDPC thus receives considerable attention from researchers and management authorities worldwide38,39,40.

Some studies investigating the water environment status of the MR of the SNWDPC have focused on aspects such as the spatiotemporal variations in the conventional water quality parameters and Chl-a41. For instance, Nong et al.13 evaluated the water quality based on measurement water quality data from 2016 to 2018 using the WQI method, with results indicating an average WQI value of was 90.39 for the MR, suggesting that the water quality was “excellent”. Tang et al.42 analyzed the water quality risks of the MR based on water quality data from 2016 to 2018 and revealed increased water quality risks during this period. Tian43 analyzed variations in the Chl-a for the MR using measurement data from 2015 and observed that Chl-a concentrations peak during the summer and autumn seasons. Nevertheless, systematic and in-depth understanding of the water environment status of the MR of the SNWDPC is still lacking: (1) The overall improvement in the water environment management of the project in recent years suggest that the water environment status may have been altered. However, studies often utilize water environment monitoring data from a single year or utilize older data, thereby limiting the ability to provide a thorough analysis of the current water environment conditions in the MR of the SNWDPC. (2) Existing studies generally focus either on water quality status or Chl-a, and as both are critical components of the water environment status and are closely related, research combining both is required to accurately ascertain the water environment characteristics of the MR.

Focusing on the MR of the SNWDPC, monthly measured water quality and Chl-a data from eight water environment monitoring sections from 2019 to 2022 were thus subjected to methods such as the WQI method, Spearman correlation analysis, and one-way analysis of variance (ANOVA) to explore spatiotemporal variations and the causes of such differences in the water environments of cross-basin water diversion projects.

The main objectives were: (1) to reveal spatiotemporal variation in the major water quality parameters and elucidate the WQI for the MR basing long time scale monitoring data; (2) to explore the spatiotemporal Chl-a variations in the project; and (3) to analyze the possible causes for changes in water quality and Chl-a systematically, and identify existing water environment problems. The findings are expected to enhance our understanding of spatiotemporal patterns in the water environment of cross-basin water diversion projects, such as the MR of the SNWDPC.

Study area

The SNWDPC is a major strategic infrastructure project that is aimed at addressing the uneven distribution of water resources in China, ensuring sustainable socioeconomic development (see Fig. 1). The MR of the SNWDPC, spanning a total length of 1432 km, sources water from the Danjiangkou Reservoir and transports it from south to north, crossing Henan, Hebei, Beijing, and Tianjin in a route that crosses eight latitudes and three temperature zones. The annual precipitation along the route ranges from 542.7 to 1173.4 mm and annual average temperature ranges from 14.6 to 21.2 °C 44. The elevation along the route ranges from 49.5 to 147.2 m. The MR of the SNWDPC is a concrete-lined, fully enclosed channel that consists mainly of open canals and various hydraulic structures such as aqueducts, inverted siphons, culverts, and tunnels45. The average width of the canal is 54 m and the average water depth is 6.95 m.

Fig. 1
figure 1

Map showing location of the MR of the SNWDPC and the eight water environment monitoring sections (generated by ArcGIS 10.7 software. URL link: https://www.arcgis.com/).

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To obtain data concerning the water quality and Chl-a, eight water environment monitoring sections were established along the MR (see Fig. 1) and denoted TC, SHN, ZW, ZHB, DAS, XHS, HNZ, and WHH from south to north. The monitoring sections are evenly distributed along the canal and can thus represent the overall water environment status of the MR.

Materials and methods

Data collection

To simultaneously evaluate the water quality and Chl-a status of the entire MR of the SNWDPC, monthly monitoring of the water quality and Chl-a concentrations was conducted at the eight water environment monitoring sections from 2019 to 2022. The number of water samples monitored was 384, which were collected at a depth of 0.5m below the water surface in the MR of the SNWDPC. Considering the representativeness of the water quality parameters and the monitoring cost constraints, data were obtained only for water temperature (T), pH, dissolved oxygen (DO), chemical oxygen demand (manganese method) (CODMn), total nitrogen (TN), and total phosphorus (TP). Previous studies have shown that concentrations of other water quality parameters, such as heavy metals, are relatively low in the MR of the SNWDPC, indicating that these six parameters alone can adequately represent the water quality status13,46. Water sample collection and analysis was performed in accordance with the Environmental Quality Standards for Surface Water37 and Standard Methods for the Examination of Water and Wastewater47.

WQI calculation

The WQI was calculated using Eq. (1), which was originally established by Pesce and Wunderlin26 and has been widely applied48,49,50. Monthly measured water quality parameters were input into Eq. (1) to obtain both monthly and average annual WQI values.

$$WQI=\frac{{\sum }_{i=1}^{n}({C}_{i}{P}_{i})}{{\sum }_{i=1}^{n}{P}_{i}}$$
(1)

\(n\) is the total number of water quality parameters; \({C}_{i}\) and \({P}_{i}\) are the normalized value and weight of water quality parameter \(i\), respectively. \({C}_{i}\) was calculated using Eq. (2). The MR of the SNWDPC is classified as a river-type water body; however, the Environmental Quality Standards for Surface Water does not provide a standard threshold for TN in river-type water bodies; thus, TN was not considered further13. Reference to the existing literature indicated \({P}_{i}\) values of 1, 1, 4, 3, and 2 for water temperature, pH, DO, CODMn, and TP, respectively51.

$${C}_{i}=\left\{\begin{array}{c}100-\left[\frac{\left({T}_{i}-{ S}_{i,k}\right)}{{ (S}_{i,k+n}-{ S}_{i,k})}\times 20j+{ I}_{i,k}\right] ,{ T}_{i}\in {[S}_{i,k } ,{ S}_{i,k+n})\\ 100-\frac{{T}_{i}}{{ S}_{i,k+n}}\times 20j , {T}_{i}\in [0 ,{ S}_{i,k})\end{array}\right.$$
(2)

\({T}_{i}\) is the measured concentration of water quality parameter \(i\); \({S}_{i,k}\) and \({S}_{i,k+n}\) are the standard thresholds of parameter \(i\) at level \(k\) and level \(k+j\) in the Environmental Quality Standards for Surface Water37, respectively; \({I}_{i,k}\) is the normalized value of parameter \(i\) at level \(k\); \(j\) is the number of equal values for the threshold, and if no equal threshold exists then \(n=1\).

The water quality status can be classified into five categories based on the WQI52: excellent, WQI ≥ 90; good, WQI ≥ 70 and < 90; moderate, WQI ≥ 50 and < 70; low, WQI ≥ 25 and < 50; bad, WQI < 25.

Statistical analysis

SPSS v27 software (IBM SPSS, USA) was used to perform statistical analysis of the average values and standard deviations obtained for the conventional water quality parameters and Chl-a in the MR of the SNWDPC. Spearman correlation analysis, a non-parametric statistical method that measures the correlation between variables, was used to investigate the relationships among the water quality parameters, relationships between the water quality parameters and Chl-a, and correlation between the water quality parameters and the WQI, allowing identification of the key parameters influencing the WQI. The ANOVA method in SPSS was applied to examine spatial differences in the water quality parameters, WQI, and Chl-a.

Results

Spatiotemporal variability of water quality parameters

The annual average values and standard deviations of the water quality parameters in the MR of the SNWDPC for the period 2019 to 2022 are shown in Table 1. The data indicate that the annual average temperature ranged from 17.25 to 18.19 °C along the route during this period, with a yearly increase observed. The annual average pH, which ranged from 8.08 to 8.35, indicates a slightly alkaline condition that also increased gradually over the years; the annual average DO was relatively high at between 9.71 and 10.30 mg/L; the annual average CODMn ranged from 1.94 mg/L to 2.16 mg/L; the annual average TN ranged from 1.05 to 1.32 mg/L, showing an increasing trend; and the annual average TP remained consistently low, ranging from 0.005 to 0.007 mg/L.

Table 1 Environmental parameters summarized as averages with standard deviation.
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The seasonal variations in the water quality parameters of the MR are depicted in Fig. 2. Seasonally, the water temperature was significantly higher in summer and autumn than spring and winter; pH levels were slightly higher in summer and autumn, and DO concentration was lowest in summer (8.59 mg/L) and highest in winter (11.42 mg/L), with winter values thus 1.33 times those in summer. Both CODMn and TN were highest in spring (2.15 mg/L and 1.22 mg/L, respectively) and lowest in autumn (1.97 mg/L and 1.12 mg/L, respectively). TP concentrations remained at a low level across all seasons, with no significant seasonal differences observed.

Fig. 2
figure 2

Seasonal variation in water quality parameters. (a) is T, (b) is pH, (c) is DO, (d) is CODMn, (e) is TN, (f) is TP.

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The spatial variability in the water quality parameters across the different monitoring sections of the MR of the SNWDPC is illustrated in Fig. 3. The spatial differences in the water temperature were not significant (ANOVA, p > 0.05) and showed a decreasing trend along the route, with the multi-year average water temperature decreasing from 18.12 °C at the southernmost TC section to 16.95 °C at the northernmost HNZ section and 16.92 °C at the WHH section. Significant spatial differences were observed in the pH (ANOVA, p < 0.05), with an increasing trend along the route that was highlighted by increases in the multi-year average from 8.09 at the TC section to 8.25 at the HNZ section and 8.20 at the WHH section. Significant spatial differences were also observed for DO (ANOVA, p < 0.05), with an increasing trend in the multi-year average along the route that was marked by a rise from 9.17 mg/L at the TC section to 10.08 mg/L at the HNZ section and 10.05 mg/L at the WHH section. Significant spatial differences (ANOVA, p < 0.05) were also observed in CODMn, which exhibited a clear increasing trend along the route and an increase in the multi-year average concentration of CODMn from 1.83 mg/L at the TC section to 2.27 mg/L at the HNZ section and 2.14 mg/L at the WHH section. The observed 0.44 mg/L and 0.31 mg/L increases correspond to rises of 24% and 17%, respectively. No significant spatial differences were observed for TN (ANOVA, p > 0.05), and its variation along the route was insignificant, with the multi-year average concentrations of TN at the TC, HNZ, and WHH sections of 1.21 mg/L, 1.17 mg/L, and 1.22 mg/L, respectively, relatively low. No significant spatial differences were observed for TP (ANOVA, p > 0.05), and its variation along the route was also insignificant, with relatively low multi-year average concentrations of 0.007, 0.006, and 0.006 mg/L observed in the TC, HNZ, and WHH sections, respectively. These spatial change trends of water quality parameters are consistent with those reported in the previous study13.

Fig. 3
figure 3

Spatial variation in water quality parameters. (a) is T, (b) is pH, (c) is DO, (d) is CODMn, (e) is TN, (f) is TP.

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Spatiotemporal WQI variation

Annual average WQI values of 92.90, 91.86, 92.22, and 91.76 were obtained for the MR of the SNWDPC in 2019, 2020, 2021 and 2022, respectively, indicating “excellent” water quality and suggesting that the MR is capable of delivering high-quality water to North China. Seasonal variations in the WQI can be seen in Fig. 4, with average WQI values of 92.88, 90.35, 92.19, and 93.32 obtained for spring, summer, autumn, and winter, respectively. The highest values in winter indicate superior water quality in this season. Spatially, no significant differences were observed in the WQI values along the MR (ANOVA, p > 0.05), although an initial increase followed by a decrease was observed along the route (see Fig. 5). The multi-year average WQI was 91.67 at the southernmost TC section, 92.82 at the mid-route ZHB section and 91.88 and 91.57 at the northernmost HNZ and WHH sections, respectively.

Fig. 4
figure 4

Seasonal variation in the WQI.

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Fig. 5
figure 5

Spatial variation in the WQI.

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Spatiotemporal Chl-a variation

Annual average Chl-a concentrations of 3.42 µg/L, 7.45 µg/L, 4.20 µg/L, and 5.23 µg/L were obtained for the MR of the SNWDPC in 2019, 2020, 2021 and 2022, respectively (see Table 1). The seasonal Chl-a variations are shown in Fig. 6, with average Chl-a concentrations of 3.44 µg/L, 8.82 µg/L, 5.55 µg/L, and 2.49 µg/L in spring, summer, autumn, and winter, respectively. The Chl-a levels were significantly higher in summer than the other seasons. The spatial variations in the multi-year average Chl-a concentrations along the MR of the SNWDPC vary significantly in space (ANOVA, p < 0.05), as seen in Fig. 7, with a clear increasing trend along the route. The multi-year average Chl-a concentration was only 3.13 µg/L in the TC section, increasing to 6.54 µg/L and 8.87 µg/L in the HNZ and WHH sections, respectively. Notably, the Chl-a concentrations reached 35.13 µg/L and 23.83 µg/L in the HNZ section during June and July in 2020, whereas concentrations reached 35.47, 42.33, 15.16, 31.70, 15.41, 25.75, and 17.93 µg/L in the WHH section during June, July, and September 2020, and June, August, September, and October 2022, respectively. The high Chl-a concentrations indicate significant eutrophication risks in the HNZ and WHH sections during certain months. The spatial variations in the Chl-a concentrations along the MR of the SNWDPC suggest that, although the Chl-a levels are low in the source area, they significantly increase as they traverse the canal.

Fig. 6
figure 6

Seasonal Chl-a variation.

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Fig. 7
figure 7

Spatial Chl-a variation.

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Discussion

Causes of variation in water quality parameters

The pH of the MR of the SNWDPC was generally alkaline from 2019 to 2022 and was slightly higher in autumn and summer compared to winter and spring. There are two main reasons for this seasonal variation. First, algal growth is more vigorous during the summer and autumn, and algae consume carbon dioxide in the water via photosynthesis, altering the acid–base balance and resulting in an increased pH. Second, the dissolution of carbon dioxide increases the acidity of water, and higher temperatures reduce the solubility of carbon dioxide, leading to the observed elevated pH53.

The annual average DO concentration ranged from 9.70 to 10.30, with high levels maintained throughout the year. This is primarily attributed to the rapid reoxygenation and the low oxygen-consuming organic matter content in the MR of the SNWDPC. Spearman correlation analysis of the relationships between the various water quality parameters over the period 2019 to 2022 indicated a negative correlation between DO and water temperature (p ≤ 0.05), as seen in Fig. 8. Water temperature is known to affect water quality54, and the sheer length of the canal results in clear gradients and significant seasonal fluctuations in the temperature, determining the saturated solubility of DO. These conclusions are consistent with those reported in other studies13.

Fig. 8
figure 8

Correlations between various water quality parameters and between water quality parameters and Chl-a.

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The concentration of TN was lower in summer and autumn than in spring and winter, mainly because algae proliferate under the ample sunlight and higher temperatures in summer and autumn. Algae absorb nitrogen from the water, reducing the TN concentration55. However, no significant variation was observed in terms of TN or TP on a spatial basis. Unlike natural rivers, the MR of the SNWDPC consists of an enclosed artificial channel that is equipped with isolation and protection nets and has no hydraulic connection with any rivers. However, the amounts of nitrogen and phosphorus entering the canal via atmospheric deposition is “heavier in the north and lighter in the south.”56 In addition, the over 700 cross-canal bridges crossing the MR of the SNWDPC collect rainwater and form runoff that eventually flows into the main canal. The different levels of TN and TP concentrations in the canal are thus likely the result of a combination of self-purification, atmospheric deposition, and bridge runoff.

CODMn is the primary water quality parameter that influences the water quality in the MR of the SNWDPC, with an increasing trend along the route. According to China’s surface water quality standards, TN is not considered in water quality evaluation for river-type water bodies37. Therefore, the water quality categories of the MR of the SNWDPC were evaluated using water temperature (T), pH, DO, TP, and CODMn. Single-factor evaluation was used to assess the water quality categories in the monitoring sections of the MR (see Fig. 9), allowing the primary water quality parameters affecting the water quality categories to be identified. As shown in Fig. 9, the water quality categories at these monitoring sections were denoted Class I–II for the period 2019 to 2022, indicating excellent water quality and meeting the national water quality category requirements. Spatially, the proportion of Class I water showed a decreasing trend from south to north, with T, pH, DO, and TP all Class I and CODMn Classes I-II. This result indicates that CODMn is the main water quality parameter that influences water quality in the project, and that it should be the focus of water environment management. As shown in Fig. 8, the Spearman coefficient of 0.33 (p ≤ 0.05) between CODMn and Chl-a, indicated a significant positive correlation, supporting the view that planktonic algae play a key role in altering the organic matter content of water57. The CODMn in the MR of the SNWDPC is sourced mainly in dissolved organic matter, which is generally from the decomposition of dead planktonic algae. Therefore, the increase in planktonic algae along the route could potentially be identified as the primary factor contributing to the observed increase in CODMn58.

Fig. 9
figure 9

Water quality categories of the monitoring sections along the MR of the SNWDPC from 2019 to 2022.

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Key water quality parameters affecting the WQI

To identify the key water quality parameters that affect the WQI in the MR of the SNWDPC, Spearman correlation analysis was conducted to examine both the correlation between the monitored water quality parameters and the WQI and the normalized water quality parameters values and the WQI (see Fig. 10). Spearman coefficients of − 0.53 (p ≤ 0.05), − 0.20 (p ≤ 0.05), 0.69 (p ≤ 0.05), − 0.49 (p ≤ 0.05), and − 0.36 (p ≤ 0.05) were obtained for the monitored T, pH, DO, CODMn, and TP and WQI, respectively. However, the obtained significant negative correlations between monitored T and pH and WQI may be incorrect. From 2019 to 2022, the monthly monitored values of T and pH met the Class I water standards of the Environmental Quality Standards for Surface Water37 in all monitoring sections of the MR. These results meant that the normalized values of \({C}_{i}\) for T and pH calculated using Eq. (2) were all 100, and the Spearman coefficients between the normalized values of T and pH and WQI were all 0 (see Fig. 10). As a result, T and pH were not identified as key water quality parameters affecting the WQI. Figure 10 indicates Spearman coefficients of 0.77 (p ≤ 0.05), 0.49 (p ≤ 0.05), and 0.36 (p ≤ 0.05) for the normalized values of DO, CODMn, TP, and WQI, respectively. In summary, the key water quality parameters affecting the WQI of the MR of the SNWDPC include DO and CODMn, with the monitored DO values showing a significant positive correlation and the monitored CODMn values showing a significant negative correlation with the WQI.

Fig. 10
figure 10

(a) correlations between monitored water quality parameters values and the WQI, and (b) normalized water quality parameters and the WQI.

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The causes of the spatiotemporal patterns of WQI in the MR of the SNWDPC can be explained by the key water quality parameters DO and CODMn. The highest WQI observed during winter may be related to the highest DO concentration and relatively low CODMn concentration in this season. The concentrations of DO and CODMn along the MR of the SNWDPC showed an increasing trend, whereas the normalized values of DO and CODMn exhibited increasing and decreasing trends, respectively, along the route. Owing to the combined effects of the increasing normalized DO and decreasing normalized CODMn, the WQI first increases and then decreases along the canal.

Causes of changes in Chl-a

Water temperature, light intensity, nutrients, pH, and flow velocity are key factors influencing the growth, community structure, and biomass of planktonic algae59. Temporally, the concentration of Chl-a in the MR of the SNWDPC was higher in summer and autumn than in spring and winter; this could be attributed to the increase in water temperature and light intensity, which promote the growth and proliferation of planktonic algae60. A significant positive correlation was also observed between water temperature and Chl-a (Spearman coefficient of 0.53, see Fig. 8), both of which were significantly higher in summer and autumn than in spring and winter, with the enhanced algal photosynthesis in the two seasons resulting in higher Chl-a concentrations.

Spatially, a clear increasing trend was observed in the concentration of Chl-a. Although the water temperature and light intensity decreased slightly along the route, the northern part of the canal shows less favorable conditions for planktonic algae growth than the southern part. Nutrients provide essential materials for the growth of planktonic algae61. However, no significant changes were observed in the concentrations of TN and TP along the canal, with Spearman coefficients of 0.01 and 0.10 obtained for TN and TP with Chl-a, respectively (see Fig. 8), indicating that neither affect Chl-a. The biomass and spatial distribution of planktonic algae are largely influenced by flow velocity. Increased flow velocity can enhance shear stress, potentially causing mechanical damage or rupturing algal cells, leading to death. Moreover, higher flow velocity can also enhance sediment resuspension, reducing the penetration of light through the water, and negatively affecting planktonic algae growth62,63. The flow velocity along the MR of the SNWDPC showed a clear decreasing trend from south to north, indicating that the flow conditions were more conducive to planktonic algae growth in the north than the south. Low pH values inhibit the growth of planktonic algae, suppressing the algal biomass, whereas high pH values favor photosynthesis, promoting the growth and proliferation of planktonic algae. The Spearman coefficient of 0.26 that was obtained for pH and Chl-a suggests a significant positive correlation. The pH in the northern section of the canal was higher than in the southern section, rendering the northern conditions more favorable for planktonic algae growth. In summary, although the water temperature and light conditions in the northern section are not conducive to planktonic algae growth compared to the southern section, the flow conditions and pH are more favorable in the north, explaining the increased Chl-a concentration along the route of the MR of the SNWDPC.

Conclusions

This study focused on the MR of the SNWDPC, one of the most prominent cross-basin water diversion projects globally. Using methods such as WQI and statistical analysis, spatiotemporal variations of the water quality parameters, WQI values, and Chl-a in the MR of the SNWDPC were analyzed over the period 2019 to 2022, and the main causes of the observed variations explored. The main conclusions are as follows: (1) The annual average WQI values for the MR of the SNWDPC ranged from 91.76 to 92.90, indicating “excellent” water quality status. The highest WQI value was observed in winter (93.32), followed by spring (92.88), autumn (92.19), and summer (90.35). The WQI values first increased and then decreased from south to north, with DO and CODMn the key water quality parameters influencing the WQI. Specifically, DO exhibited a significant positive correlation and CODMn had a significant negative correlation with WQI. (2) The concentration of Chl-a in the MR of the SNWDPC was significantly higher in summer than the other seasons and showed a clear increasing trend along the route from south to north. Water temperature and light intensity are the main factors affecting the seasonal variation of Chl-a, whereas flow conditions and pH are the main drivers of its spatial distribution.

In conclusion, the water quality status of the MR of the SNWDPC is “excellent”, with the water quality consistently maintained in Classes I–II, indicating a healthy water environment overall. However, the northern section, particularly the end of the canal, is prone to high concentrations of CODMn and Chl-a, which pose environmental risks. Water environment management authorities should thus focus on these areas to ensure the safety of the water supply along the MR of the SNWDPC. In the future, research needs to further investigate the correlation between water quality parameters and Chl-a in the MR of the SNWDPC. Especially using control experimental method to quantitatively reveal the relationship between CODMn and algal growth and death.