Abstract

In order to evaluate the overall impact of water diversion on Taihu Lake, this paper carries out numerical simulation of the temporal and spatial distribution of the flow field and the TP concentration field in Taihu Lake based on measured data during the water diversion period by using the Euler–Lagrange method. The results show that: (1) the Pearson coefficient of monitoring points in the East Lake area increases significantly during the water diversion period, indicating that the diversion of water may indirectly influence water quality; (2) the diversion of water has a significant influence on the flow rate of the simulated stations in Taihu Lake, and the influence is Taipu (103%) > Gongwan (60%) >East Lake (31%); (3) when the amount of water flow transfer from the Wangyu River to the lake is greater than 100 m3/s, the mean concentration of TP in the Center and East lakes increases significantly (more than 50%). The recommended water diversion discharge is to be controlled in the range 100–200 m3/s and the total water diversion yield between 1.56 and 2.59 billion m3/a.

INTRODUCTION

The protection of water resources has a variety of criteria for depth, flow area and water quality (Phillips et al. 2016; Jalil et al. 2018; Ke et al. 2019). The high seasonal elevation caused by water diversion changes water temperature and illumination, which is not conducive to the health of aquatic plants (especially submerged plants) (Li et al. 2019; Liu et al. 2019). Water exchange caused by frequent water diversion also influences the original aquatic ecological structure (Dai et al. 2017). The TP concentration of the Yangtze River at the Changshu water control project is currently much higher than that of the Central and Eastern areas of Taihu Lake (Yang et al. 2018). The diversion of the Wangyu River has become a local TP pollution source for the East Taihu Lake, and the diversion period is generally in the dry season, leading to a rapid change of water due to low elevation. As a result, the degradation coefficient is generally lower than in the wet season (Song et al. 2009; Yan et al. 2016). Resuspension of sediment caused by excessive water diversion is also one of the reasons for the high TP concentration (Huang et al. 2015). In particular, this issue has become increasingly common in recent years due to the disruption of water diversion (Chen et al. 2018). There is an immediate need to measure the overall impact of water diversion in the future and to suggest effective water diversion.

Scholars have done a lot of research on the diversion of the Yangtze River (Yu et al. 2018), and they have made some scientific explanations for the effects on the recipients (Wu et al. 2018; Yao et al. 2018). Li et al. (2013a, 2013b) used the EFDC model to study and analyze the scheduling optimization of water diversion from the Yangtze River to Taihu Lake, and found that drainage through Meiliangwan Bay could increase the dilution of pollution. Li et al. (2013a, 2013b) carried out simulation studies on different water diversion routes on the environmental-economics aspect. Qi et al. (2016) used the EFDC model to research the factors influencing the water age of Poyang Lake, and found that increasing the quantity of water could have a significant impact on hydrodynamics. Based on actual measurement, Qin et al. (2018) found that water diversion brings not only water resources, but also many toxic and harmful substances, such as antibiotics, which have adverse effects on aquatic organisms and water quality. Tang et al. (2016) simulated the nutritive salt input conditions and found that reducing the intake of nutrient salt has certain inhibitory effects on the outbreak of cyanobacteria. Dai et al. (2018) argued that the water diversion has adverse effects on Taihu Lake by studying the effects of the diversion of the Wangyu River for creatures on the shoreline. Hydrodynamic assessment metrics include, in particular, elevation, flow area, water age and water exchange (Zhang et al. 2016; Gao et al. 2018). Water exchange has a higher practical value than the age of water. Existing water exchange studies are primarily divided into the Lagrange and Euler methods (Christian et al. 2008; Cucco et al. 2009), but lack some validity. In general, there is a limited amount of research on the full flow field and concentration field quantitative study of the advantages and disadvantages of different water diversion amounts. There is no sound basis and scientific account of the effects of the final diversion of the Yangtze River to Taihu Lake project.

Based on the Pearson coefficient method, this study carries out a correlation analysis on measured data from the past eight years to determine the influence of the actual water diversion period on Taihu Lake. Combined with the hydrological conditions and meteorological conditions of the water diversion stage in 2017, the Euler–Lagrange model is used to quantitatively study the flow field, water exchange, TP concentration field and migration trajectory of the contaminated mass under different water diversion discharges in the ‘Yangtze River diversion to Taihu Lake’ in the Wangyu River.

RESEARCH AREA

Taihu Lake (30°05′–32°08′N, 119°08′–122°55′E) is the third largest freshwater lake in China. The maximum and minimum bottom elevations are 1.95 m and 0.75 m, the average water depth of the whole lake is 1.9 m, while the average water depth of the eastern bay is 0.9 m (Huang et al. 2016; Feng et al. 2018). It has great significance in shipping, tourism, culture, scientific research, etc. Every year, it provides nearly 3 billion m3 of drinking water to Shanghai, Suzhou and Jiaxing. However, since the cyanobacteria crisis in Taihu Lake in 2007, drinking water safety has become a key task in the management of Taihu Lake (Gao et al. 2017; Janssen et al. 2017). The prevailing winds over Taihu Lake are a southeasterly wind and northwesterly wind, influenced by the subtropical monsoon climate, the maximum and minimum temperatures in the study area are −6.2 and 34 °C, the annual mean temperature of Lake Taihu varies from 14.9 to 16.2 °C, and its average annual evaporation has been 1,219 mm in the last ten years, which means 2.85 billion cubic metres of water will go with evaporation (Li et al. 2015; Xu et al. 2019). The average annual rainfall (AAR) of Taihu Lake Basin in the past 30 years has been 1,254 mm. There has been a significant positive correlation between the total lake intake (TLI) and the total water diversion (TWD) water volume in the study area in the past 30 years (Figure 1), and the water exchange cycle has also decreased from 300 days to 190 days (Jiang et al. 2018).

Figure 1

Study area and water diversion information (simulated stations based on the monitoring stations of water quality and surface elevation).

Figure 1

Study area and water diversion information (simulated stations based on the monitoring stations of water quality and surface elevation).

RESEARCH METHODS

Taihu model building method

Based on a three-direction incompressible Navier–Stokes equation with uniform distribution of Reynolds values, this study is subject to the assumption of Boussinesq and hydrostatic pressure. The finite volume method of the three-dimensional hydrodynamic model is used to calculate spatial discretization, and the coupled water quality model is as follows (Wang et al. 2014): 
formula
(1)
where: C – contaminant concentration; u, v, w – flow velocity components in x, y and z directions; Ex, Ey, Ez – diffusion coefficients in x, y and z directions; S – source sink term; F – biochemical term.
The introduction of sediment resuspension in the model is mainly reflected in the treatment of the source sink term: 
formula
(2)
where: S is the release of phosphorus pollutants in each lake area; is the proportion of phosphorus pollutant release to sediments in each lake area; Yi is the resuspension flux of sediments in each lake area (g/(m2·d)); Ai is the sediment area in each lake area (m2).

Water exchange study method

The water exchange rate is also a key indicator for evaluating hydrodynamics. This study refined the water diversion and the water exchange rate of each lake area by using the water exchange method. The specific method is (Yuan et al. 2018): 
formula
(3)
where: EX – water exchange rate; C – material concentration; H – water depth; m – grid number in the statistical area; n – number at specific time.

Construction of Lagrange particle tracking model

The Lagrange particle tracking (LPT) method (Huhn et al. 2016) is used to study the trajectory of the pollution group. The Lagrange particle tracking equation is: 
formula
(4)
where: a is the drift term, b is the diffusion term, and ɛ is the random coefficient. 
formula
(5)
for n = 1, 2, 3, …. according to the Euler formula with drift term a and diffusion coefficient b. is the normal distribution Gaussian increment of the Brownian motion w, which is a Gaussian random process with independent increment in the subinterval .

Pearson coefficient method

The Pearson coefficient is used to calculate the correlation between the TP concentrations of 15 assessment sections in Taihu Lake. The specific formula is as follows (Jia et al. 2018): 
formula
(6)
where: r is the correlation coefficient between two points; xi and yi are the annual average sequence of salinity at the study point; n is the number of years; lxx and lyy are the sum of the squares of variables x and y; and lxy is the sum of squares of variables x and y. The larger the absolute value of the correlation coefficient, the stronger the correlation. The closer the correlation coefficient is to 1 or −1, the stronger the correlation. The closer the correlation coefficient is to 0, the weaker the correlation. The degree of correlation has the following cases: when 0.8 ≤ │r│<1, it is regarded as extremely strong correlation; when 0.6 ≤ │r│<0.8, it is regarded as strong correlation; when 0.4 ≤ │r│<0.6, it is considered as moderate correlation; when 0.2 ≤ │r│ < 0.4, it is regarded as weak correlation; when and │r│<0.2, the variables have extremely weak correlation or have no correlation.

Wangyu River diversion scene design

In this study, a Taihu model is built with 5,881 triangular elements and 3,146 nodes. According to the data provided by the Taihu Lake Basin Authority of the Ministry of Water Resources (http://www.tba.gov.cn/channels/43.html) and the China Meteorological Data Network (http://data.cma.cn/user/modpwd.html), the discharge and water quality in the study area are determined by the measured data. Combined with the actual duration of water diversion from the Wangyu River in 2017, the total simulation time is determined to be 60 days. The wind field is set as no wind, 4 m/s southeast wind (SE) and 4 m/s northwest wind (NW). The flow rate, TP concentration and the water exchange rate are calculated by using the Euler method, and the migration trajectory of the pollution group is calculated by the Lagrange method (Table 1).

Table 1

Prediction scene setting

ProgramDischarge from Wangyu River (m3/s)Simulation methodInput water quantity and quality in Western Lake area (m3/s)Wind fieldOther rivers inflowing to and outflowing from the lakeOutput result
0/50/100/150/200/250/300/350/400 Euler method Measured data of the 31 rivers in 2017 No wind The water volumes outflowing from the Taipu and the Xujiang River increase correspondingly according to the actual ratio. Flow rate, TP concentration 
SE 
NW 
0/200 Euler method No wind Water exchange rate 
SE 
NW 
0/200 Lagrange method No wind Migration trajectory of the contaminated mass 
SE 
NW 
ProgramDischarge from Wangyu River (m3/s)Simulation methodInput water quantity and quality in Western Lake area (m3/s)Wind fieldOther rivers inflowing to and outflowing from the lakeOutput result
0/50/100/150/200/250/300/350/400 Euler method Measured data of the 31 rivers in 2017 No wind The water volumes outflowing from the Taipu and the Xujiang River increase correspondingly according to the actual ratio. Flow rate, TP concentration 
SE 
NW 
0/200 Euler method No wind Water exchange rate 
SE 
NW 
0/200 Lagrange method No wind Migration trajectory of the contaminated mass 
SE 
NW 

RESULTS AND DISCUSSION

Model parameter calibration and correlation analysis of monitoring points

In order to ensure that the hydrodynamic model can satisfy more study, the water body is divided into 5,881 non-structural grids (triangular elements) and 3,146 nodes at a spatial resolution of 300 m, and the turbulent model is large eddy simulation (horizontal direction) and log law (vertical direction). The measured data (surface elevation, discharge of 31 rivers, wind field, precipitation and evaporation) of 2017 are combined. After simulating the elevation of Taihu Lake, the turbulence, roughness and wind drag coefficient are calibrated as 0.28, 0.02 m and 0.003, respectively. The simulation results are well matched with the measured data elevation at each monitoring station (Figure 2). The flow field structure simulated under the wind field of the southeast monsoon and the northwest monsoon at 5 m/s speed has exactly the same circulation shape as the measured results. The results (Figure 3) in circulation flow direction and velocity are also consistent with the results reported in previous studies (Huang et al. 2015).

Figure 2

Calibration and verification of surface elevation in 2017.

Figure 2

Calibration and verification of surface elevation in 2017.

Figure 3

Verification of flow field.

Figure 3

Verification of flow field.

To further compare the simulated elevation with the measured values, this study uses three model evaluation methods, namely, average relative error (MRE), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). The evaluation process involves an error and correlation analysis of the measured values (M) and simulated values (S), with the following formulae (Li et al. 2018): 
formula
(7)
 
formula
(8)
 
formula
(9)
where: N is the number of times of total simulation; i is the number of times of simulation; Si is the value of the ith simulation; Mi is the value of the ith measurement; and is the measured average value.

The evaluation results of the monitoring stations (Table 2) show that the simulated elevations fit well with the measured data (the highest error <0.09 m). The results can represent more than 94% of the actual situation. Hence the hydrodynamic model can meet the requirements for the subsequent research.

Table 2

Model calculation evaluation of surface elevation

MonitorRMSEMRENSE
Xishan 0.056 4.66% 0.975 
Dapu 0.039 3.06% 0.982 
Taipu 0.049 3.51% 0.974 
Wangting 0.059 4.37% 0.943 
MonitorRMSEMRENSE
Xishan 0.056 4.66% 0.975 
Dapu 0.039 3.06% 0.982 
Taipu 0.049 3.51% 0.974 
Wangting 0.059 4.37% 0.943 

According to the model calibration results (Figure 4 and Appendix), when the TP sediment release coefficient is 0.15 g/(m2·d), and the TP degradation coefficient is 0.01–0.03/d (Wang et al. 2017), and the three-direction diffusion coefficient is 1 m2/s, the MRE and NSE of 15 national monitoring points is about 4.97% and 0.867. There were four water diversion stages in 2017. The calculated error at the monitoring point of XD is the most obvious, and its MRE and NSE are about 6.55% and 0.853; the error of all results can explain more than 75% of scenes, indicating that the constructed model can meet the requirement for further research.

Figure 4

Model calculation evaluation of TP concentration in 2017.

Figure 4

Model calculation evaluation of TP concentration in 2017.

The Pearson coefficient test is used to conduct a correlation study of the TP data calculated at 15 points in Taihu Lake from 2011 to 2018 (Figure 5). The results show that ZSW has a direct influence on DP under the three wind conditions, but has a weak correlation with other points of the lake. ZSW is a bay in the lake, and the main source of water exchange is with the river channels, but it has a low exchange probability in other regions. By using XH as a study object, the sorting order according to the maximum correlation is as follows: 
formula
 
formula
Figure 5

TP correlation coefficients of 15 stations in Taihu Lake in 2011–2018 (the left figure is the annual statistical data; the right figure is the statistical data of the water diversion period).

Figure 5

TP correlation coefficients of 15 stations in Taihu Lake in 2011–2018 (the left figure is the annual statistical data; the right figure is the statistical data of the water diversion period).

Compared with the measured data for the whole year, it is found that the correlation coefficient between the points of the two lines increased significantly during the diversion phase, suggesting that the strengthening of the water exchange has had some effect on the TP concentration in the East Lake area. The area of the East Lake has the best quality of water in Taihu Lake. As a result, the increase in correlation will inevitably lead to an increase in the flow of pollutants. It can be inferred that excessive water diversion not only improves hydrodynamics, but also indirectly affects the quality of the water and increases the diffusion capacity of cyanobacteria. TP is a restrictive indicator for a cyanobacterial outbreak. There is a certain synergistic mechanism between cyanobacteria and TP (Zhao et al. 2019). Increased concentration of TP can increase the probability of large-scale eutrophication.

Influence of water diversion on flow field and water exchange rate in Taihu Lake

By studying hydrodynamics and water exchange, it is found that the flow rates of Taipu, Gongwan (GW) and the East Lake area (East) have significant positive associations with the amount of water diversion in wind-free and northwest wind conditions (Figure 6). In the southeast wind condition, the flow rate in GW decreases first and then increases with the wind speed. The explanation for this is that the diversion of water slowly removes the wind field that controls the flow rate in GW. The Taipu flow rate is about 0.030–0.042 m/s, the GW flow rate is between 0.012 and 0.030 m/s and the East average flow rate shift is about 0.011 m/s. Compared with the average flow rate of 0.035 m/s in Taihu Lake, the water diversion has already had some influence on the flow rate of some areas within the lake. The influence of the water diversion on the flow rate is Taipu (103%) > GW (60%) > East (31%).

Figure 6

Effects of water diversion on flow rate at monitoring stations in eight areas (no wind, 5 m/s SE and 5 m/s NW are set from left to right figures).

Figure 6

Effects of water diversion on flow rate at monitoring stations in eight areas (no wind, 5 m/s SE and 5 m/s NW are set from left to right figures).

The water exchange study shows that water diversion can significantly increase the water exchange rate under the three most conventional wind conditions of Taihu Lake. Water exchange rates have risen from 28%, 30% and 29% to 51%, 49% and 50% respectively, indicating that the water diversion has a greater impact on water exchange than on the wind field (Li et al. 2018). According to the definition of the water exchange cycle by Luff & Pohlman (1995), when the inflow water volume reaches 200 m3/s, the Taihu Lake water exchange period is approximately 120 d, which is much shorter (60%) than the previous 300 d. Based on the simulated results (Figure 7), it can be seen that the water exchange region contains two lake areas of good water quality and a relatively stable wetland ecosystem, namely the Central area (Center) and the East. A key period for the growth of aquatic plants is the dry season of the diversion period. Anti-seasonal high surface elevation is caused by water diversion and turbidity increases due to resuspension of sediment, which eventually affects the required light conditions and the healthy growth of aquatic plants (Shi et al. 2015; Luo et al. 2018).

Figure 7

Simulation results of water exchange rate of the whole lake under discharge of inflow water of 0 and 200 m3/s.

Figure 7

Simulation results of water exchange rate of the whole lake under discharge of inflow water of 0 and 200 m3/s.

Influence of water diversion on the spatial and temporal distribution of the TP concentration field in Taihu Lake

In order to further analyze the correlation between water diversion and TP in each lake area, the Lagrange particle simulation pollution group is conducted. The results (Figure 8) show that water diversion will accelerate the movement of the polluted mass in major areas, reduce the self-cleaning time of the water, and increase the likelihood that the Center and the East will be contaminated. Water diversion may increase the migration rate of the polluted mass in Meiliangwan Bay (MLW), Zhushanwan Bay (ZSW) and the Northwest area (NW) to the Center and East, under the conditions of the northwest wind. As a result, there is a large increase in the number of polluted mass particles in the East. Contaminated mass in the Southwest area (SW) may influence the Western Lake area in the southeasterly and northwesterly wind conditions. It is therefore recommended that the prevention and monitoring of pollution sources in the limited watershed of the Tiaoxi River be improved. In order to better address the pollution and reduce the impact of the polluted mass on the Center and East, a water ecosystem in the Western Lake area should be created.

Figure 8

LTP simulation results of migration trajectories of contaminated mass in each lake area at 0 and 200 m3/s.

Figure 8

LTP simulation results of migration trajectories of contaminated mass in each lake area at 0 and 200 m3/s.

The simulation results of the Euler method (Figure 9) indicate that under the condition of no wind, only the TP concentration of GW shifts under the influence of a water diversion of 150 m3/s. The water body comes from East Taihu Lake and Taipu, and the TP cannot be completely destroyed due to the large flow of water. The TP concentration in East Taihu Lake doubles to 200 m3/s. When the diversion discharge reaches 300 m3/s, the Center and the SW are pressured by water. The pollutants in the NW cannot be filtered or depleted effectively. The TP concentration in the lake is greater than 0.05 mg/L (III Standard).

Figure 9

Influence of water diversion of TP concentration at monitoring stations in eight main areas (no wind, 5 m/s SE and 5 m/s NW are set from left to right figures).

Figure 9

Influence of water diversion of TP concentration at monitoring stations in eight main areas (no wind, 5 m/s SE and 5 m/s NW are set from left to right figures).

In the southeast wind condition, when the water diversion discharge is 100 m3/s, the TP concentration in MLW will increase by 50%, and the TP concentration in NW and SW, ZSW and Taipu will rise rapidly under the influence of the lake flow. In the case of northwesterly winds, when the water diversion discharge is 100 m3/s, the concentration of TP in the center and east is increased by more than 50%. Consequently, the TP concentration in the SW decreases, because the majority of the pollutants in the Western Lake area are generated by the flow to the Center and East. In order to ensure a TP concentration cap, it is recommended that the water diversion discharge be controlled in the range 100–200 m3/s, and the total water diversion yield at 60 d be controlled at 1.56–2.59 billion m3.

Based on the proportional relationship between the water diversion discharge and the main area effect (Figure 10), combined with the weighted average estimate, the main water diversion impact areas in the three dominant wind fields and proportions are: GW (53.84%) > East (19.46%) > Taipu (9.85%) > Center (8.75%) > SW (4.63%) > MLW (4.57%). In the absence of wind, the outflow is primarily on the east side of Taihu Lake. With the increase in the discharge and yield of water drainage, the proportion of the Center and East will increase rapidly and the final rate of change will be higher than in the case of wind. Wind-induced lake currents can neutralize the influence of some pollutants, indicating that hydrodynamics is an important factor on local water quality. Due to the uncertainty of real hydrology and water quality, there is no simple linear relationship between hydrodynamics and water quality. Excessive diversion of water would ultimately increase confusion.

Figure 10

Variation of influence proportions of water diversion on eight stations.

Figure 10

Variation of influence proportions of water diversion on eight stations.

Water diversion will guarantee the need for downstream water supply and accelerate the hydrodynamic processes of the lake. Nonetheless, due to the weak water ecosystem in most areas of Taihu Lake, the polluted mass in the Western Lake area is likely to enter the Center and East more quickly due to the rapid flow rate and shifts in the erratic wind field. Specifically, the concentration of TP in the Center and East will be much higher due to the diversion of water. In fact, a large-scale eutrophication phenomenon occurs due to the interaction between algae and TP. This explains why eutrophication became more extreme even though fewer contaminants reached the Taihu Lake basin in 2017.

CONCLUSIONS AND PROSPECT

  • (1)

    The influence of the diversion of water on the flow rate at the simulated Taihu Lake stations is Taipu > GW > East and the influence of the flow rate at each point is 31–103%. Water diversion will increase the exchange rate of water between the Center and South. In the meantime, water diversion will cause high anti-seasonal elevation and impede the regeneration of aquatic ecosystems. It is therefore assumed that water diversion may promote the possibility of a large-area diffusion of cyanobacteria blooms appearing.

  • (2)

    The Pearson coefficient of simulation stations in the East significantly increases during the water diversion phase, suggesting that the hydrodynamic force has an indirect effect on water quality. Water diversion is not always beneficial to the enhancement of TP water quality. When the water diversion discharge from the Wangyu River to the lake is greater than 100 m3/s, the mean concentration of TP in the Center and East increases significantly (more than 50%). In combination with the proportional relationship between the water diversion yield and the actual water inflow to the lake in the last ten years, it is recommended that the water diversion discharge be controlled between 100 and 200 m3/s, and the total diversion water yield be controlled between 15.6 and 25.9 billion m3/a. In the meantime, pollution control and ecological restoration should be carried out along the Wangyu River in order to improve the quality of the water inflow.

ACKNOWLEDGEMENTS

The authors thank the Chinese National Science Foundation (No. 51879070), the Fundamental Research Funds for the Central Universities (No. 2019B44214) and PAPD.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this paper is available online at https://dx.doi.org/10.2166/ws.2020.031.

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Supplementary data