1. Introduction
Water environment deterioration is a prominent issue in river basin management throughout the world, which has become a serious threat to water security [
1]. Surface water and groundwater is affected by geological, climatic, and other natural conditions as well as anthropogenic activities [
2] such as precipitation, the pumping of groundwater, and regional droughts [
3,
4], which is of great significance to the ecological environment of the basin and the production and life of residents in the surrounding areas [
5]. The assessment of long-term water quality variation and identification of probable causes can provide information supports and references for sustainable water resources management.
The single factor index method [
6,
7], comprehensive pollution index method [
8,
9], Canadian Council of Ministers of the Environment Water Quality Index (CCME CWQI) [
10,
11], multivariate statistical techniques [
12,
13], such as cluster analysis [
14], discriminant analysis [
12], factor analysis [
13], principal component analysis [
15], and artificial neural network [
16,
17] are widely used in river water quality evaluation. DRASTIC is a widely used indexing method to assess groundwater vulnerability to a wide range of potential contaminants [
18,
19]. All these methods are used to comprehensively assess the water quality as well as identify spatial and temporal variations in water quality and main sources of contamination.
Wavelet analysis is becoming a common tool for analyzing localized variations of power within a time series [
20,
21], which is widely used in hydrology in the study of noise elimination, filtering of time series, monitoring of abrupt points, identification of periodic components [
22,
23], and assessing the long-term variation of water quality [
24,
25]. Besides, wavelet analysis is also combined with Artificial Neural Network (wavelet-ANN), Adaptive Neuro-Fuzzy Inference System (wavelet-ANFIS), or extreme learning machine to predict monthly water quality, which is successfully used in the Aji-Chay River in Northwestern Iran [
26,
27], the Yamuna river in India [
28], and the Johor River in Malaysia [
29].
In previous studies on temporal variation of water quality, multivariate statistical techniques and continuous wavelet transform are used to present a significant and validated picture of the seasonal periodic behavior of water quality, but they do not directly explore long-term periods, variation tendency, and the coherence of the periodic behavior of water quality variables with influencing factors. This present study aims to remedy this shortcoming by investigating long-term periods, variation tendency, and the coherence of water quality with water level and water diversion from outside the basin with combined methods of continuous wavelet transform, cross wavelet transform, wavelet coherence, Mann–Kendall trend test, and Sen’s slope estimator.
The Lixiahe abdominal area is a representative plain river network in the lower reaches of Huai River, where the water used for industry and agriculture is mainly from water diversion from the Yangtze River. The water diversion project is widely constructed to solve the problems of regional water shortage and water pollution in many countries, including Australia, China, Canada, India, the United States, and others [
30]. Hence, assessment of the effects of the water diversion on the regional water resource and water quality is significant for sustainable water resources management [
31,
32]. Since the water diversion project in the Lixiahe abdominal area has been executed for decades, it is valuable to investigate the long-term water quality variation and its possible causes to give scientific guidelines for water resources management and the optimization of water diversion operation. Based on the monthly water quality data of 15 monitoring stations from 2003 to 2017, the comprehensive water quality index (CWQI) was used to evaluate the water quality of the river, and the methods of wavelet analysis, Mann–Kendall trend test, and Sen’s slope estimator were used to study periodic characteristics and tendency of water quality variation. Furthermore, the possible causes of water quality variation were discussed.
4. Possible Causes of Water Quality Variation
The temporal and spatial variation of water quality is closely related to the discharge and accumulation of pollutants, the local water resources from precipitation, and the amount of water from external sources. Therefore, the following discussion focuses on the impact of water pollutant input, water level, and the amount of diversion water on water quality.
4.1. Input of Regional Water Pollutants
It is not easy to obtain the regional short-term pollutant input; thus, the annual load of four main indicators (COD
Mn, NH
3-N, TN, and TP) of the Lixiahe abdominal area was calculated according to the Jiangsu Statistical Yearbook (
Figure 5). During 2003–2017, the discharge of COD
Mn and TP increased by 54% and 39% respectively, while the increase in NH
3-N and TN was slightly smaller, 27% and 24%. The growth rate of point source pollutants from industrial and municipal wastewater emissions is about 35%. The main point source pollutant inputs are COD
Mn and TP, and the amount of NH
3-N and TN is relatively small. Among the non-point source pollutants, the growth rate of COD
Mn and TP is relatively large, 70% and 47% respectively, and the growth rate of NH
3-N and TN is relatively small, about 24%. In general, with the booming of the social economy, the main pollutants discharged to river water have increased significantly, which may induce water environment deterioration.
4.2. Correlation Analysis of Water Quality and Water Level
The water level reflects the amount of water in the area, which is influenced by multi-processes of precipitation, water diversion, and drainage, and it has an important impact on the water quality. The correlation of the CWQI and water level of each station are analyzed by XWT and WTC analysis by identifying the resonance period. The XWT and WTC reflect the resonance signal characteristics and the correlation coefficient of the CWQI and water level in the high-energy area and low-energy area, respectively. The results of XWT and WTC analysis at some stations are shown in
Figure 6, and the correlation between the CWQI and water level at each station is summarized in
Table 7.
In the Taizhou area, except for HGAF, the CWQI and water level of the other seven stations have a resonance period of 12–14 months, CLDY, LTZZ, and XTLT have positive correlations, XGSG and XTBM have negative correlations, SGSX has negative and positive correlations in the high-energy area and low-energy area respectively, TDQT has a transition from negative correlation to positive correlation in the high-energy area and positive correlation in the low-energy area. In the Yancheng area, there is a 12–14-month resonance period between the CWQI and water level at four monitoring stations, GGDG and YJMD have positive correlations, XTHT has a negative correlation, SYYX has positive and negative correlations in the high-energy area and low-energy area, respectively. In the Yangzhou area, there are 12–14-month resonance periods between the CWQI and water level of three monitoring stations, BSZS and BCSD have a negative correlation, and SYDG has a positive correlation.
In general, the CWQI has a resonance period of about 12 months with water level, with positive correlation accounting for 62% and negative correlation accounting for 38%. The change of correlation in different periods is mainly related to the water level being affected by multiple factors, such as local water resources, water diversion, and drainage. The increase in local water recourse due to precipitation induces the water level rise and water quality deterioration with an increase in the CWQI, due to large amount of non-point source pollutants entering the river with rainfall runoff. The increase in diversion water leads to water level rise and water quality improvement with an increase in the CWQI. Hence, the CWQI and water level is mainly negatively correlated in the wet season, when the water resource is mainly from rainfall runoff, while the CWQI and water level is mainly positively correlated in the dry season, when the water resource is mainly from water diversion.
4.3. Correlation Analysis of Water Quality and Amount of Diversion Water
The Lixiahe abdominal area is in the upstream section of south-to-north water diversion from the Yangtze River in Jiangsu Province. Thus, the water quality of the area is improved by introducing the good-quality Yangtze River water. The correlation of the CWQI and water diversion volume of each station is analyzed by XWT and WTC analysis, by identifying the resonance period. The results of XWT and WTC analysis of the CWQI and water diversion volume at some stations are shown in
Figure 7. The correlation between the CWQI and diversion water at each station is summarized in
Table 8.
In the Taizhou area, except for the high-energy area of HGAF, there is a resonance period of 11–12 months for the CWQI and water diversion volume of each station; CLDY, HGAF, LTZZ, SGSX and TDQT have inverse correlation, the CWQI of XGSG lags behind the water diversion volume by one-quarter of a cycle, while XTBM and XTLT have different correlation in the high-energy area and low-energy area. In the Yancheng area, there is a 12–13-month resonance period between the CWQI and water diversion volume at four monitoring stations. Here, XTHT has positive correlation, GGDG and YJMD have negative correlation, SYYX has different correlations in the high-energy area and low-energy area. In the Yangzhou area, the resonance period of the CWQI and water diversion volume of three monitoring stations is 12–14 months, BSZS has a positive correlation, SYDG has a negative correlation, and the CWQI of BCSD lags behind the water diversion volume by one-quarter of a cycle.
In general, there is a resonance period of about 12 months between the CWQI and water diversion volume at each monitoring station. Two-thirds of the 15 monitoring stations are located in the main channels of water diversion and nearby rivers, and one-third is far away from the main channels of water diversion. The CWQI of the monitoring stations at the water diversion route and its nearby river is inversely related to the water diversion volume. The larger the water diversion volume, the better the water quality. The water diversion from the Yangtze River plays an important role in improving the regional water environment.
Due to the complexity of the water quality variation and the limitation on the methodology, this study was qualitative when assessing the possible causes of water quality variation. Nevertheless, water diversion is proved to be an important contributor to improving the regional water environment. Further work is required to investigate the quantitative relationship between the water quality and intensity and duration of water diversion to provide scientific guidance on the optimization of water diversion operation.
5. Conclusions
From 2003 to 2017, the water quality variation in the Lixiahe abdominal area contains multi-scale periodic fluctuations of 3–59 months, and the seasonal variation of 12 months is significant at most stations. The CWQI of 7 out of 15 monitoring stations has a significant decreasing trend, and the trend slope ranges from −0.071/yr to 0.007/yr. The water quality of the main routes of the water diversion and the nearby rivers has significantly improved, while the water quality of rivers far away from the main routes, which is less affected by the water diversion, has no obvious improvement, or even becomes worse.
The CWQI and water level is mainly positively correlated in the wet season, when the water resource is mainly from rainfall runoff that brings many non-point pollutants, while the CWQI and water level are mainly inversely correlated in the dry season, when the water resource is mainly from water diversion from the Yangtze River. The CWQI and the water diversion volume are inversely related at monitoring stations in the main routes of water diversion and its nearby river. Hence, water diversion plays an important role in improving the regional water environment.
With the booming of the social economy in the Lixiahe abdominal area, the main pollutants discharged to river water have increased, but the water quality has generally improved, especially in the main routes of water diversion and its nearby rivers, due to the water diversion from the Yangtze River. Hence, the key to regional water environment improvement lies in the systematic control of point and non-point source pollutants and the optimization of water diversion operation.