The algal blooms in the Middle and Lower reaches of the Hanjiang River (MLHR) have become a great aquatic eco-environmental problem over recent years. The planned implementation of the Water Diversion Project from the Three Gorges Reservoir to the Hanjiang River (WDP-TH) would influence the growth and propagation of diatoms and the development of algal blooms. In this paper, a dynamic model of diatom growth is developed that considers factors such as light exposure, water temperature, total phosphorus, and flow velocity. The effects of the WDP-TH on algal density and blooms in the MLHR were predicted for a typical median year, a typical dry year, and an extremely dry year. The results showed that following the water recharge in a typical median year, the propagation of diatoms in the MLHR was significantly promoted because of an increase in total phosphorus concentration. However, following the water recharge in typical dry and extremely dry years, increased flow rate and improved hydrodynamic conditions significantly inhibited diatom growth and accelerated the transport process. The results of the hypothetical simulation showed that after the initiation of the WDP-TH, inhibition effects of improved hydrodynamic force on algae were more apparent than promotion effects of increased total phosphorus concentration in the MLHR.

  • The diatom dynamic model considering factors scuch as light, water temperature, phosphorus, and flow velocity, and performs well.

  • The probability of algal blooms in the Hanjiang River might increase following the water recharge in a typical median year.

  • Inhibition effects of improved hydrodynamic force on algae dominate after the initiation of the project.

Over the development of human society, the phenomenon of water eutrophication has become increasingly prominent, which is commonly followed by algal blooms. Algal blooms have become a global aquatic eco-environmental problem, which seriously threatens water safety (Vörösmarty et al. 2010; Xia et al. 2019). River blooms were less common than lake and reservoir blooms (Mitrovic et al. 2008; Jung et al. 2009; Yang et al. 2011). However, due to urbanization and rapid economic development, algal blooms appear in many rivers both in China and abroad (Yang et al. 2011; Xue et al. 2023). Furthermore, because of the dense interweaving of river systems and strong hydrodynamic force, river blooms are often characterized by wide impacts, complex outbreak causes, and great difficulties associated with their control (Li et al. 2020); usually, river blooms have more serious negative impacts.

The Hanjiang River is one of the most important tributaries of the Yangtze River. Danjiangkou Reservoir (DJK) in the upper reaches of the Hanjiang River is the water source of the middle route of the South-to-North Water Diversion Project (SNWDP). Algae blooms have occurred frequently in the Middle and Lower Reaches of the Hanjiang River (MLHR) in recent years, which has become the most important aquatic eco-environmental problem and has attracted much attention because of the sensitivity and uniqueness of the region. It was found that river blooms are dominated by diatoms with low-temperature tolerance and occur mostly in winter and spring (Yang et al. 2011). Outbreaks of algal blooms in large rivers are usually closely related to changes in the hydrological regime (Ferris & Lehman 2007).

Many researches have been conducted about the driving factors of algae blooms in the Hanjiang River. Generally high nutrient concentration is not regarded as a restricted factor that results in algal blooms in the Hanjiang River. The blooms are more apparently affected by external factors such as hydrology and meteorology (Li et al. 2020). Cheng et al. (2019) identified the water level of the Yangtze River estuary, total phosphorus (TP) levels, and water temperature as the main influencing factors of algal blooms. Xin et al. (2020) identified phosphorus as a key limiting factor for algae growth, and velocity threshold values of 0.74 m/s or a flow rate of 800 m3/s were proposed for the Xiantao section. The water level difference between Xiantao and Hankou was identified as an important factor and the response time of blooms to driving factors was found to be about 10 days (Xia et al. 2020; Liu et al. 2022).

Mitigation measures for the spreading of algae blooms in the Hanjiang River have been promoted in recent years. Xie et al. (2005) examined the impacts of SNWDP on algal blooms in the MLHR based on a model to calculate the blooms occurrence probability, and their results showed that the probability increased after water diversion. Yang et al. (2012) established a generalized additive model to analyze the dynamic responses of algae to environmental changes. Li et al. (2022) proposed an ecological scheduling scheme for the prevention of blooms by increasing the discharge from DJK and regulating fluctuations in the water level.

At present, research on the mechanism underlying explosive algae growth, as well as simulations of algae growth dynamics are rare. Yang et al. (2012) proposed a more water-saving discharge scheme for the DJK to prevent algal blooms. Xia et al. (2022) added hydrological factors into a traditional riverine algae growth model and combined it with a one-dimensional distributed hydrological model; algae growth under different SNWDP schemes was simulated and found that the SNWDP would significantly increase the possibility of bloom outbreaks.

The Water Diversion Project from the Three Gorges Reservoir to the Hanjiang River (WDP-TH) is the follow-up water source project of the SNWDP. Operating with cascade reservoirs of the Hanjiang River and other water diversion projects, WDP-TH will further change the hydrodynamic force, water quality, water temperature, and other conditions that restrict the growth and propagation of algae in the MLHR, affecting both the occurrence and development of algal blooms. This paper focuses on the impacts of WDP-TH operation. A mathematical model is constructed to quantitatively study the spatio-temporal distribution and variation of algal density under different scenarios. Ultimately, the effect of WDP-TH on diatom blooms in the MLHR is described. The results of this study provide a reference for the coordinated dispatch of water projects to both prevent and control algal blooms in the MLHR.

Originating from the southern foot of the Qinling Mountain, Hanjiang River is the longest tributary of the Yangtze River, with a total length of 1,577 km and a catchment area of 159,000 km2. The upper reaches of the Hanjiang River are located upstream of the Danjiangkou Dam, and the MLHR is located downstream of the Danjiangkou Dam. The MLHR is located between an east longitude of 111–115° and a north latitude of 30–33°. The river length of the MLHR is 652 km, and its catchment area is 64,000 km2. Annual average runoff measured at the Huangzhuang hydrographic station is about 46.7 billion m3. The geographical position of the MLHR is high in the northwest and low in the southeast, featuring mountains, hills, plains, wetlands, and other landforms. The rivers are densely covered and interwoven by gullies. The main tributaries are the Nanhe River, Beihe River, Tangbai River, Xiaoqing River, Manhe River, Zhupi River, and Hanbei River. The construction of the following seven cascade reservoirs from the upper to lower reaches of the MLHR has been planned: Danjiangkou, WangFuzhou, Xinji, Cuijiaying, Yakou, Nianpanshan, and Xinglong, of which Xinji and Nianpanshan are under construction, while the other cascade reservoirs have been completed. The water source of the SNWDP is the DJK, and since the completion of SNWDP-Ⅰ, more than 58 billion m3 of water have been transferred to Beijing, Tianjin, Henan, and Hebei from December 2014 to August 2023, directly benefiting about 150 million people. As a follow-up water source of the SNWDP, the water intake of the WDP-TH is located at the Longtanxi River on the northern bank of the Three Gorges Reservoir. A 194.8 km water tunnel is used to divert water to the Anle River Estuary on the right bank of the Hanjiang River, which is located about 5 km downstream of the Danjiangkou Dam. The average annual water diversion volume is 3.90 billion m3, and the designed discharge at the canal head is 170–212 m3/s. The WDP-TH diverts water from the Three Gorges Reservoir to the Hanjiang River to improve the water resource allocation capacity of the Hanjiang River basin. After the implementation of the project, part of the discharge from DJK was replenished by the Three Gorges Reservoir, thus increasing the water diversion of the SNWDP to the north and improving the water supply capacity of the SNWDP. A schematic diagram of the study area is shown in Figure 1.
Figure 1

Study area (WDP-TH, Water Diversion Project from the Three Gorges Reservoir to the Hanjiang River; SNWDP, South-to-North Water Diversion Project; WTP-YH: Water Transfer Project from the Yangtze River to the Hanjiang River).

Figure 1

Study area (WDP-TH, Water Diversion Project from the Three Gorges Reservoir to the Hanjiang River; SNWDP, South-to-North Water Diversion Project; WTP-YH: Water Transfer Project from the Yangtze River to the Hanjiang River).

Close modal

In recent years, the water quality of the MLHR was generally fine, and the annual water quality basically reached Class II of ‘Environmental quality standards for surface water’. In certain parts and at specific times, the water quality was Class III, and TP and ammonia nitrogen was identified as the main limiting factors. At present, algae blooms are the main eco-environmental problem in this basin. Since the first large-scale algal blooms were reported in February 1992, there have been 11 large-scale algal blooms outbreaks until 2021, most of which occurred from January to March during dry seasons in late winter and early spring. Recently, because of the influence of multiple factors, such as water depletion, cascade reservoir development, water diversion, and backwater in the estuary, the blooms outbreak frequency has shown an increasing trend. Moreover, the occurrence range has also moved from the lower reaches of the Qianjiang to the Xinglong Reservoir. In addition to water pollution, algae can easily block the filters of water plants along the river, affecting the normal water supply.

Study scenarios

This study was divided into two scenarios: one with WDP-TH and another without WDP-TH. The study year is 2035, which is the year designated for WDP-TH completion. Considering the inflow and discharge of the DJK as well as the water recharge process of the WDP-TH, six calculation schemes were developed in both scenarios by selecting a typical median water year, a typical dry year, and a typical extremely dry year. The frequency analysis of the natural annual inflow of DJK from 1956 to 2016 showed that the water inflow frequency of 50, 90 and 98% responded to 1979, 1995 and 1999, respectively. Then the representative years of the typical median water year, typical dry year and typical extremely dry year were 1979, 1995 and 1999, respectively. Each scheme includes Danjiangkou, WangFuzhou, Xinji, Cui Jiaying, Yakou, Nianpanshan, and Xinglong reservoirs, which should all be in operation by 2035. The study area covers 652 km of the MLHR mainstream, reaching from the Danjiangkou Dam to the Hanjiang River estuary. The calculation time is from January to April during the period of the Hanjiang algae bloom.

Modeling method

Algal density is a key index that reflects algal blooms. In reference to the algal blooms occurrence in the MLHR, the most dominant algal group in algal blooms in the Hanjiang River over the years is diatoms (Li et al. 2020); therefore, in this paper, diatoms were selected as representative species for simulation analysis. Based on a model of hydrodynamic force, water temperature, and water quality, algal density was predicted in combination with a mathematical model of the growth dynamics of algae, thus providing a reference for the effects of algal blooms.

The transport equation of a two-dimensional mathematical model of algal density is shown as follows:
(1)
where t is the time (s); h is the water depth (m); u and v are flow velocity in both x direction and y direction, respectively (m/s); Ex and Ey are turbulent diffusion coefficients in both x direction and y direction, respectively (m2/s); CB is the diatom biomass (mg/L); SC is the growth source term of diatoms (mg/(L·s)), which mainly considers the growth dynamics process of algae. Its expression is as follows:
(2)
where P is the growth rate of diatoms (s−1); BM is the basal metabolic rate of diatoms (s−1); PR is the prey rate of diatoms (s−1).
The diatom growth rate is central for model construction, and it is also affected by many external environmental factors. At present, it is generally assumed that external factors causing diatom mass reproduction and bloom outbreaks mainly include hydrodynamic force, nutrient salts, and meteorological conditions (Hisman et al. 2018; Xia et al. 2020). According to prior research, hydrodynamic conditions such as flow rate and flow velocity greatly affect the growth and transport of algae (Kim et al. 2017). High nutrient concentrations form a necessary condition for the outbreak of algal blooms, and the phosphorus content is a key limiting factor (Xia et al. 2019). Meteorological conditions such as light exposure and temperature are also important environmental factors that affect algae accumulation (Jiang et al. 2008). Water temperature, which is mainly affected by air temperature, is an important factor that affects the growth or repopulation of algae. Based on this information, the four limiting factors of light exposure, water temperature, TP concentration, and flow velocity were mainly considered in the synthetic function of the diatom growth rate. The diatom growth rate can be expressed as:
(3)
where PM is the diatom growth rate under optimal conditions (d−1); f1(I) is the light exposure limiting factor; f2(T) is the temperature limiting factor; f3(CTP) is the TP concentration limiting factor; f4(v) is the flow velocity limiting factor.
Limiting factor expressions of light, temperature, TP, and flow velocity are as follows:
(4)
(5)
(6)
(7)
where I is the solar radiation intensity (W/m2); IS is the most suitable light intensity; T is the temperature (°C); TM1 is the minimum suitable temperature for diatom growth (°C); TM2 is the maximum suitable temperature for diatom growth (°C); KTG1 is the growth effect of diatoms when the temperature is below TM1 (°C−2); KTG2 is the growth effect of diatoms when the temperature exceeds TM2 (°C−2); CTP is the concentration of TP (mg/L); C1* is the lower limit of the TP concentration threshold for diatom growth (mg/L); C2* is the limiting condition of TP concentration for diatom growth (when the TP concentration exceeds C2*, algal growth is completely unrestricted by TP concentration) (mg/L); v is the flow velocity (m/s); v* is the critical flow velocity of blooms occurrence (when flow velocity ≥ v*, algal growth is limited) (m/s).

In the model, algal growth, metabolism, predation rate, temperature, light exposure, nutrient limit, and other parameters were mainly obtained through calibration. The critical flow velocity was determined according to the relationship between bloom occurrence and flow velocity in the MLHR over the years. In reference to the simulation results of velocity distribution during previous bloom occurrences (Li et al. 2022), the average flow velocity at Xiantao in the early stage of bloom outbreaks ranged from 0.33 to 0.51 m/s, and the average flow velocity was 0.67 m/s when blooms subsided. Considering the least favorable condition, the critical velocity v* in Hanjiang River was 0.67 m/s.

Critical condition

In the scenario without WDP-TH, the discharge process of the DJK in each typical year is adopted for the flow boundary; for the water quality boundary, monthly water quality monitoring data of the section downstream of the Danjiangkou Dam in the last 3 years was adopted. Furthermore, the influences of major tributaries, such as the Beihe River, Nanhe River, Xiaoqing River, Tangbai River, Manhe River, Zhupi River, and Hanbei River, as well as the Water Transfer Project from the Yangtze River to the Hanjiang River (WTP-YH) on the flow and water quality of the mainstream of the Hanjiang River are considered. To calculate the algal density boundary of DJK, discharge diatom biomass data were used, measured downstream of the Danjiangkou Dam from April to May 2020. In the scenario with WDP-TH, except for the above boundary conditions, in the flow boundary, the flow incoming from the Three Gorges in each typical year is added. Additionally, the water quality boundary adopts water quality monitoring data of Yinxingtuo upstream of the Three Gorges Dam over the last three years, and the algae density boundary adopts diatom biomass data from April to May 2020 of Longtanxi, where the water intake of the WDP-TH is located. The discharge flow rates from DJK and WDP-TH of each typical year are shown in Table 1 and Figure 2. Scenarios of S1, S3 and S5 represent median, dry and extreme dry years without WDP-TH, respectively; scenarios of S2, S4 and S6 represent median, dry and extreme dry years with WDP-TH, respectively.
Table 1

The discharge flow rates from DJK and WDP-TH

MonthMedian water year (1979)
Dry year (1995)
Extreme dry year (1999)
S1S2
S3S4
S5S6
DJKDJKWDP-THTotalDJKDJKWDP-THTotalDJKDJKWDP-THTotal
Jan.E 490 228 262 490 490 228 262 490 138 228 262 490 
Jan.M 490 228 262 490 490 228 262 490 104 228 262 490 
Jan.L 490 228 262 490 490 228 262 490 120 228 262 490 
Feb.E 590 330 262 592 400 228 262 490 118 174 262 436 
Feb.M 492 232 262 494 400 238 262 499 123 174 262 436 
Feb.L 491 231 262 493 400 373 262 635 107 174 262 436 
Mar.E 491 231 262 493 400 259 262 521 94 174 262 436 
Mar.M 491 231 262 493 182 263 262 525 96 96 262 358 
Mar.L 490 228 262 490 174 228 262 490 86 72 263 335 
Apr.E 490 228 262 490 490 228 262 490 174 174 263 437 
Apr.M 490 228 262 490 490 248 262 510 321 174 261 435 
Apr.L 490 234 256 490 490 316 256 572 174 174 256 430 
MonthMedian water year (1979)
Dry year (1995)
Extreme dry year (1999)
S1S2
S3S4
S5S6
DJKDJKWDP-THTotalDJKDJKWDP-THTotalDJKDJKWDP-THTotal
Jan.E 490 228 262 490 490 228 262 490 138 228 262 490 
Jan.M 490 228 262 490 490 228 262 490 104 228 262 490 
Jan.L 490 228 262 490 490 228 262 490 120 228 262 490 
Feb.E 590 330 262 592 400 228 262 490 118 174 262 436 
Feb.M 492 232 262 494 400 238 262 499 123 174 262 436 
Feb.L 491 231 262 493 400 373 262 635 107 174 262 436 
Mar.E 491 231 262 493 400 259 262 521 94 174 262 436 
Mar.M 491 231 262 493 182 263 262 525 96 96 262 358 
Mar.L 490 228 262 490 174 228 262 490 86 72 263 335 
Apr.E 490 228 262 490 490 228 262 490 174 174 263 437 
Apr.M 490 228 262 490 490 248 262 510 321 174 261 435 
Apr.L 490 234 256 490 490 316 256 572 174 174 256 430 
Figure 2

The flow rate from the DJK and WDP-TH.

Figure 2

The flow rate from the DJK and WDP-TH.

Close modal

Parameter calibration and verification

Daily monitoring data of flow rate, water quality, algae density, and chlorophyll-a at Shayang and Xiantao in MLHR during the diatom outbreak period from February 13 to March 7, 2018, were selected to calibrate the algae growth model. Daily monitoring data of algae density from January 19 to January 30, 2021, were selected for verification. In addition, as initial values of algae growth, metabolism, and predation rate, default values of the model were used, and initial values of temperature, light exposure, and nutrient limits were estimated based on monitoring data of bloom occurrence years in the MLHR from 2004 to 2018. Parameters were obtained as shown in Table 2.

Table 2

Calibration results of parameters for the algae model

ParametersDefinitionResultsUnit
PM Optimum growth rate of diatoms 4.0 d−1 
BM Basal metabolic rate of diatoms 0.01 d−1 
PR Diatom predation rate 0.01 d-1 
IS Optimum light intensity 50 lan/d 
TM1 Minimum optimum temperature for diatom growth 6.6 °C 
TM2 Maximum optimum temperature for diatom growth 11.3 °C 
KTG1 Growth effect when temperature is below TM1 1.25 °C−2 
KTG2 Growth effect when temperature is above TM2 0.7 °C−2 
C1* Lower threshold for algae growth 0.03 mg/L 
C2* Upper threshold for algae growth 0.075 mg/L 
ParametersDefinitionResultsUnit
PM Optimum growth rate of diatoms 4.0 d−1 
BM Basal metabolic rate of diatoms 0.01 d−1 
PR Diatom predation rate 0.01 d-1 
IS Optimum light intensity 50 lan/d 
TM1 Minimum optimum temperature for diatom growth 6.6 °C 
TM2 Maximum optimum temperature for diatom growth 11.3 °C 
KTG1 Growth effect when temperature is below TM1 1.25 °C−2 
KTG2 Growth effect when temperature is above TM2 0.7 °C−2 
C1* Lower threshold for algae growth 0.03 mg/L 
C2* Upper threshold for algae growth 0.075 mg/L 

A comparison between simulated and measured values of algae density at Shayang and Xiantao is shown in Figures 3 and 4. Nash efficiency coefficient and mean relative error were used to evaluate and verify the simulation. In the simulation period of 2018, the Nash efficiency coefficients of Shayang and Xiantao were 0.841 and 0.792, respectively, while the mean relative errors were 11.5 and 6.8%, respectively. In the validation period of 2021, the Nash efficiency coefficients of Shayang and Xiantao were 0.948 and 0.786, respectively, and the mean relative errors were 2.0 and 9.3%, respectively. In general, the algae growth process as simulated by the model is close to the actual situation, and thus, the model can be used to simulate and predict algae density in the MLHR. There were relatively large errors between simulated and measured values in 2018, mainly resulting from the many external factors that affect diatom growth. The model mainly considers four dominant factors: light exposure, water temperature, TP, and flow velocity. Moreover, during the blooms outbreak of 2018, Danjiangkou, Xinglong, and other cascade reservoirs implemented emergency water dispatch; therefore, the model failed to accurately simulate the whole process of water dispatch. Further, calculation results were also affected to a certain extent.
Figure 3

Comparison between simulated and measured algal density values at Shayang and Xiantao (simulation period during 2018).

Figure 3

Comparison between simulated and measured algal density values at Shayang and Xiantao (simulation period during 2018).

Close modal
Figure 4

Comparison between simulated and measured algal density values at Shayang and Xiantao (validation period during 2021).

Figure 4

Comparison between simulated and measured algal density values at Shayang and Xiantao (validation period during 2021).

Close modal

Analysis of change in peak values of algae density

The effect of water diversion to the MLHR on algae density in typical years was considered under the maintenance of the current water quality level of the Three Gorges Reservoir. Table 3 shows a comparison of peak values of algal density for January to April at five main control sections of the MLHR under two scenarios. The peak values at each section along the MLHR increase gradually from upper to lower reaches under both scenarios. This result is mainly caused by the gradual increase of nutrient concentrations along the MLHR (such as TP), as well as by the deceleration of flow velocity caused by backwater from the Yangtze River and the Xinglong Reservoir storage. Taking the scenario with WDP-TH as an example, the peak values gradually increased from 75 × 104 to 1,128 × 104 cell/L in a typical median year, and from 67 × 104 to 863 × 104 cell/L in a typical dry year, showing an increase of about 12–14 times from upper to lower reaches.

Table 3

Prediction results of peak values of algal density at a typical section; unit: 104 cell/L

Typical yearScenariosXiangyangHuangzhuangShayangQianjiangXiantao
Median water year Without WDP-TH(S1) 73 179 518 826 989 
With WDP-TH(S2) 75 212 634 1,048 1,128 
Changes after project initiation 2.7% 18.4% 22.4% 26.9% 14.1% 
Dry year Without WDP-TH(S3) 85 281 747 1,189 1,418 
With WDP-TH(S4) 67 152 460 725 863 
Changes after project initiation − 21.2% − 45.9% − 38.4% − 39.0% − 39.1% 
Extremely dry year Without WDP-TH(S5) 102 319 848 1,341 1,606 
With WDP-TH(S6) 77 234 603 951 1,138 
Changes after project initiation − 24.5% − 26.6% − 28.9% − 29.1% − 29.1% 
Typical yearScenariosXiangyangHuangzhuangShayangQianjiangXiantao
Median water year Without WDP-TH(S1) 73 179 518 826 989 
With WDP-TH(S2) 75 212 634 1,048 1,128 
Changes after project initiation 2.7% 18.4% 22.4% 26.9% 14.1% 
Dry year Without WDP-TH(S3) 85 281 747 1,189 1,418 
With WDP-TH(S4) 67 152 460 725 863 
Changes after project initiation − 21.2% − 45.9% − 38.4% − 39.0% − 39.1% 
Extremely dry year Without WDP-TH(S5) 102 319 848 1,341 1,606 
With WDP-TH(S6) 77 234 603 951 1,138 
Changes after project initiation − 24.5% − 26.6% − 28.9% − 29.1% − 29.1% 

After the implementation of the WDP-TH, the peak values increased in the typical median water year but decreased in typical dry and extremely dry years. Following water diversion in a typical median water year, the peak value at Xiangyang increased slightly (by 2.7%), and peak values at Huangzhuang, Shayang, Qianjiang, and Xiantao (downstream of Xiangyang) increased by 14.1–26.9%. Increases were especially pronounced at Shayang and Qianjiang. Under the scenario with WDP-TH, the peak values at Qianjiang and Xiantao reached 1,048 × 104 and 1,128 × 104 cell/L, respectively. Following the water diversion in typical dry and extremely dry years, the peak values at Xiangyang, Huangzhuang, Shayang, Qianjiang, and Xiantao decreased by 21.2–45.9%. These decreases were relatively large downstream of Huangzhuang in the typical dry year, all of which were about 40%. In the scenario with WDP-TH, the peak values at Qianjiang and Xiantao in a typical dry year and at Qianjiang in an extremely dry year decreased to less than 1,000 × 104 cell/L.

Analysis of the change process of algal density

Figure 5 shows the simulation results of the algal density process at main control sections from January to April in a typical median water year. The results show that the densities first increased and then gradually decreased from January to April at all sections. Peak values occurred from late January to early February, and the second highest values occurred from late February to early March. Under both scenarios, the processes were basically identical. Taking Shayang as an example, in the scenario without WDP-TH, algae density increased rapidly from 250 × 104 cell/L on January 1 to its peak value of 518 × 104 cell/L on February 2, then slowly decreased to 424 × 104 cell/L until February 18, and then slowly increased to the second highest value of 443 × 104 cell/L until March 2. After that, algae density gradually decreased, and on April 30, it was only 96 × 104 cell/L. In the scenario with WDP-TH, algae densities at Shayang from January to April were higher overall and the change trend was similar to that observed in the scenario without WDP-TH. In each period, algae densities increased by 22.2–29.7% compared with densities in the scenario without WDP-TH.
Figure 5

Process of algae densities at main control sections in the middle and lower reaches of the Hanjiang River (MLHR) from January to April in a typical median water year.

Figure 5

Process of algae densities at main control sections in the middle and lower reaches of the Hanjiang River (MLHR) from January to April in a typical median water year.

Close modal
Figure 6 shows the simulation results of the algal density process at main control sections from January to April in typical dry and extremely dry years. The results show that the densities first increased and then decreased, following an unimodal pattern. The difference is that in the scenario with WDP-TH, algae densities mostly peaked in early March, while in the scenario without WDP-TH, they basically peaked about one month earlier in early February. Algal density processes were generally the same in both scenarios, and both peak values occurred in early February. Still taking Shayang as an example, in the scenario without WDP-TH in a dry year, algae densities gradually increased from 181 × 104 cell/L on January 1 to 747 × 104 cell/L on March 4, and then rapidly decreased to 203 × 104 cell/L until March 14. After that, algae densities fluctuated slightly in the range of 130 × 104 cell/L to 200 × 104 cell/L until April 30, when the trend became relatively smooth in general. Under the scenario with WDP-TH, algae density at Shayang gradually increased from 259 × 104 cell/L on January 1 to 460 × 104 cell/L on February 5, then rapidly decreased to 242 × 104 cell/L on February 18, and then slowly decreased to 108 × 104 cell/L on April 30. Compared to the scenario without WDP-TH, algae densities were relatively high from January to about mid-February, relatively low from mid-February to mid-March, and generally similar after mid-March. However, in an extremely dry year, the density change at Shayang was different. Under the scenario without WDP-TH in an extremely dry year, algal densities rapidly increased from 278 × 104 cell/L on January 1 to their peak value of 848 × 104 cell/L on February 5, and then gradually decreased to 140 × 104 cell/L until April 30. In the scenario with WDP-TH, algae densities decreased, and the change trend was similar to that without WDP-TH. Compared with the scenario without WDP-TH, the algae densities were 28.9–50.7% lower in each period.
Figure 6

Process of algal densities at main control sections in the MLHR from January to April in a typical dry year and an extremely dry year.

Figure 6

Process of algal densities at main control sections in the MLHR from January to April in a typical dry year and an extremely dry year.

Close modal

Analysis of the possible situation of algal blooms

In reference to the classification of the algal blooms degree and the technical specifications for monitoring, an algae density ≥ 107cell/L is assumed as a critical value for blooms occurrence. Possible situations of algal blooms at five main control sections in the MLHR from January to April were analyzed for both scenarios, and the results are shown in Table 4. In both scenarios, in each typical year, algal densities at Xiangyang, Huangzhuang, and Shayang did not exceed 107 cell/L, and there were basically no algal blooms.

Table 4

Analysis of time windows when algae densities exceed 107 cell/L at Qianjiang and Xiantao in typical years

Typical yearScenarioAlgae densities at Qianjiang
Algae densities at Xiantao
Days when algae densities exceed 107 cell/LDuration when algae densities exceed 107 cell/LChange range (104 cell/L)Days when algae densities exceed 107 cell/LDuration when algae densities exceed 107 cell/LChange range (104 cell/L)
Median water year Without WDP-TH(S1) 
With WDP-TH(S2) February 3–8 1,004–1,048 19 February 4–16 and March 4–9 1,001–1,128 
Dry year Without WDP-TH(S3) 10 January 28–March 3 1,037–1,189 15 February 27–March 13 1,009–1,418 
With WDP-TH(S4) 
Extremely dry year Without WDP-TH(S5) 35 January 28–March 3 1,006–1,341 45 January 26–March 11 1,024–1,606 
With WDP-TH(S6) 10 February 6–15 1,016–1,138 
Typical yearScenarioAlgae densities at Qianjiang
Algae densities at Xiantao
Days when algae densities exceed 107 cell/LDuration when algae densities exceed 107 cell/LChange range (104 cell/L)Days when algae densities exceed 107 cell/LDuration when algae densities exceed 107 cell/LChange range (104 cell/L)
Median water year Without WDP-TH(S1) 
With WDP-TH(S2) February 3–8 1,004–1,048 19 February 4–16 and March 4–9 1,001–1,128 
Dry year Without WDP-TH(S3) 10 January 28–March 3 1,037–1,189 15 February 27–March 13 1,009–1,418 
With WDP-TH(S4) 
Extremely dry year Without WDP-TH(S5) 35 January 28–March 3 1,006–1,341 45 January 26–March 11 1,024–1,606 
With WDP-TH(S6) 10 February 6–15 1,016–1,138 

In a typical median water year, algae densities at Qianjiang and Xiantao did not exceed 107 cell/L in the scenario without WDP-TH, while in the scenario with WDP-TH, on six days (February 3–8), the densities exceeded 107cell/L at Qianjiang and on 19 days (February 4–16 and March 4–9) at Xiantao. After initiation of the WDP-TH, if external conditions remain unchanged, the algal bloom occurrence possibility in the reaches downstream of Shayang may increase from January to April in typical median water years.

In the scenario without WDP-TH, in a typical dry year, there may be 10 days (February 28–March 9) when algae blooms (i.e., algae density ≥ 107 cell/L) may occur at Qianjiang and 15 days (February 27–March 13) when it may occur at Xiantao; in a typical extremely dry year, there may be 35 days (January 28–March 3) at Qianjiang and 45 days (January 26–March 11) at Xiantao when algae blooms may occur. In the scenario with WDP-TH, algal densities at Qianjiang and Xiantao in a typical dry year and at Qianjiang in a typical extremely dry year were all less than 107 cell/L. The number of days when algal blooms can occur at Xiantao in a typical extremely dry year was reduced to 10 days (February 6–15). After WDP-TH initiation, if external conditions remain unchanged, the occurrence possibility of algal blooms in the reaches downstream of Shayang may decrease from January to April in both typical dry and extremely dry years.

The Hanjiang River, where algae blooms are much more severe, has attracted much attention, especially in the MLHR, where the water resources development and utilization degree is much higher. On the other hand, the TP concentration from upstream to downstream gradually increases in the MLHR, resulting in an increase in the risk of algal bloom. In recent years, due to the superposition of the SNWDP and the cascade development, the hydrological regime and water morphology of the MLHR have changed significantly. WDP-TH transfers water from the Three Gorges Reservoir to the Hanjiang River, which further changes the hydrodynamic force and nutrients in the MLHR. These changes affect algae growth and the occurrence of algal blooms. Therefore, taking the MLHR as the research object, based on the superposition of water diversion and cascade development, the effects of changes in hydrodynamic conditions and TP conditions on algal density are discussed, which is a typical example in the study of river blooms.

Relationship between TP and algal density

The sensitivity of algal density in response to changes in TP concentration was explored by the comparison of algal density changes under the same hydrodynamic conditions but under different scenarios of TP concentration. S3 and S4 were selected as the base scenarios. In order to compare with S3, Scenario S7 is added, in which the TP concentration of DJK is replaced by the TP concentration of WDP-TH. In order to compare with S4, Scenario S8 is added, in which the TP concentration of WDP-TH is replaced by the TP concentration of DJK.

Figure 7 shows the critical condition of TP of DJK and WDP-TH. The assumed calculation conditions are shown in Table 5. Because the TP concentration of WDP-TH is higher at 0.049 ∼ 0.076 mg/L than that of DJK, there is a higher TP concentration in Scenario S7, and there is a lower TP concentration in Scenario S8.
Table 5

Simulation analysis of the sensitivity of algal density in MLHR

ScenariosCritical condition of TP
Critical condition of hydrodynamic forceRemark of scenarios
DJKWDP-TH
S3 TP of DJK – Discharge flow of DJK Low TP + weak hydrodynamic force 
S7 TP of WDP-TH – Discharge flow of DJK High TP + weak hydrodynamic force 
S4 TP of DJK TP of WDP-TH Discharge flow of DJK + water recharge of WDP-TH High TP + strong hydrodynamic force 
S8 TP of DJK TP of DJK Discharge flow of DJK + water recharge of WDP-TH Low TP + strong hydrodynamic force 
ScenariosCritical condition of TP
Critical condition of hydrodynamic forceRemark of scenarios
DJKWDP-TH
S3 TP of DJK – Discharge flow of DJK Low TP + weak hydrodynamic force 
S7 TP of WDP-TH – Discharge flow of DJK High TP + weak hydrodynamic force 
S4 TP of DJK TP of WDP-TH Discharge flow of DJK + water recharge of WDP-TH High TP + strong hydrodynamic force 
S8 TP of DJK TP of DJK Discharge flow of DJK + water recharge of WDP-TH Low TP + strong hydrodynamic force 
Figure 7

Critical condition of TP of DJK and WDP-TH.

Figure 7

Critical condition of TP of DJK and WDP-TH.

Close modal
The relationship between peak values of algal density and TP concentration is shown in Figure 8(a). In Scenario S3, the peak values of algal density in Huangzhuang and Xiantao were 281 × 104 and 1,418 × 104cell/L, respectively, when the TP concentrations were 0.038 and 0.051 mg/L, respectively. In Scenario S7, the peak values of algal density in Huangzhuang and Xiantao were 360 × 104 and 1,800 × 104cell/L, respectively, when the TP concentrations were 0.060 and 0.066 mg/L, respectively. Compared to Scenario S3, in Scenario S7 the TP concentrations in Huangzhuang and Xiantao increased by 0.022 and 0.015 mg/L, respectively, and the peak values of algal density increased by 79 × 104 and 382 × 104cell/L, respectively. In Scenario S4, the peak values of algal density in Huangzhuang and Xiantao were 152 × 104cell/L and 863 × 104cell/L, respectively, when the TP concentrations were 0.043 and 0.051 mg/L, respectively. In Scenario S8, the peak values of algal density in Huangzhuang and Xiantao were 124 × 104cell/L and 695 × 104cell/L, respectively, when the TP concentrations were 0.035 and 0.041 mg/L, respectively. Compared to Scenario S4, in Scenario S8 the TP concentrations in Huangzhuang and Xiantao reduced by 0.008 and 0.010 mg/L, respectively, and the peak values of algal density reduced by 28 × 104 and 168 × 104cell/L, respectively.
Figure 8

Influence of TP concentration on the peak values of algal density.

Figure 8

Influence of TP concentration on the peak values of algal density.

Close modal

The results of the relationship between changes rangeability of peak values of algal density and changes rangeability of TP concentration are shown in Figure 8(b). Compared to Scenario S3, in Scenario S7 the changes rangeability of TP concentration in Huangzhuang and Xiantao were 57.2 and 30.9% respectively, and the changes in rangeability of peak values of algal density in Huangzhuang and Xiantao were 27.8 and 27.0% respectively. Compared to Scenario S4, in Scenario S8 the changes in rangeability of TP concentration in Huangzhuang and Xiantao were −17.8 and −18.5% respectively, and the changes in rangeability of algal density peak in Huangzhuang and Xiantao were −18.8 and −19.4% respectively.

The analysis showed that the peak values of algal density increased with increasing TP concentration, under the same other conditions. A positive correlation was evident between the changes rangeability of peak values of algal density and TP concentration. It demonstrated that a high concentration of nutrients is necessary for the occurrence of diatom bloom, one important similarity between river-type diatom blooms and lake-reservoir diatom blooms (Smith 2003).

Contribution of hydrodynamic conditions and TP to algal blooms

Researchers pointed out that, unlike lentic ecosystems such as lakes and reservoirs, physical factors such as river hydrological conditions and temperature are often more important than factors such as water nutrition level (Reynolds et al. 1994). In the following content, the combined effects of hydrodynamic conditions with flow rate and nutrient conditions with TP on algal blooms in the MLRH were further analyzed.

Figure 9 shows the relationship between peak values of algal density and flow rate in Scenarios S1-S6. The highest and lowest peak values of algal density in the Huangzhuang section were 318 × 104cell/L and 152 × 104cell/L, respectively, which appeared in Scenarios S5 and S4, and the flow rates were 162 and 589 m3/s, respectively. The highest and lowest peak values of algal density in the Xiantao section were 1,606 × 104cell/L and 863 × 104cell/L, respectively, which appeared in Scenarios S5 and S4, and the flow rates were 491 and 617 m3/s, respectively.
Figure 9

Relationship between algal density peak and flow rate.

Figure 9

Relationship between algal density peak and flow rate.

Close modal
Figure 10 shows the relationship between peak values of algal density and TP concentration in Scenarios S1–S6. The TP concentrations corresponding to the highest and lowest algal density in the Huangzhuang section were 0.038 and 0.046 mg/L, respectively. The TP concentrations corresponding to the highest and lowest algal density in the Xiantao section were 0.106 and 0.051 mg/L, respectively.
Figure 10

Relationship between algal density peak and TP concentration.

Figure 10

Relationship between algal density peak and TP concentration.

Close modal

The analysis shows a negative correlation between algal density peak and flow rate. This is particularly evident in the Xiantao section with higher algal density. Because the higher flow rate has a more obvious scour effect on algae, it helps to avoid algae enrichment. Furthermore, a high flow rate may have some inhibitory effect on algal growth. The relation between the peak algal density and TP concentration was weaker than that of hydrodynamic, demonstrating that the dominant factor of blooms in the MLHR is hydrodynamic conditions rather than TP concentration.

From the comparison of S3 to S8 listed in Table 5, it was found that under the condition of fixed low TP input but increased flow rate, the flow rate in Huangzhuang section increased by 63 m3/s (12% increase), and the peak value of algae density decreased by 158 × 104cell/L (56% decrease). The flow rate in the Xiantao section increased by 121 m3/s (24.4% increase), and the peak value of algae density decreased by 722 × 104cell/L (51% decrease). This further indicates that the enhancement of hydrodynamic conditions in the MLHR in dry years has a more obvious inhibitory effect on algae growth than the promoting effect of the increase of TP concentration. Yang et al. (2011) pointed out that hydrological conditions are proved to be the most important factors affecting diatom blooms in rivers, and the operation of water conservancy projects has a particularly significant impact on hydrological conditions and diatom blooms, which is consistent with the conclusion in this manuscript.

Based on the research results, it can be seen that enhancing the hydrodynamic conditions and reducing the TP load are the keys to controlling algal blooms in the MLHR. If measures can be taken to increase the flow rate and reduce the TP concentration at the same time in the algal blooms prone period from January to April, the synergistic effect of inhibiting algal proliferation will be generated and a better control effect will be achieved. According to the scheduling rules of WDP-TH, there is more water recharge in dry years and less water recharge in normal years. If the surplus water volume can be used to recharge the MLHR by WDP-TH during the dry period in the median water year, the problem of increasing algal blooms risk caused by the increase of TP concentration after the water diversion in the median water year can be alleviated. Therefore, it is suggested to optimize the joint scheduling mode of WDP-TH and DJK, which can make use of the surplus water volume to recharge the MLHR from January to March, so as to reduce the probability of blooms occurrence as much as possible by enhancing the hydrodynamic conditions. At the same time, it is important to strengthen the control of internal and non-point source pollution and reduce the TP load from tributaries, which are important measures of algal bloom control for the long term.

By integrating the characteristics of diatom blooms in the MLHR, a mathematical model was constructed to simulate the growth dynamics of diatoms. This model focused on quantifying the effects of light exposure, water temperature, TP, and flow velocity on the growth, metabolism, and predation of diatoms. The spatial and temporal distribution and changes of algae density in the MLHR were simulated in two scenarios (with or without WDP-TH) in typical median water, dry, and extremely dry years. Moreover, the influence of hydrodynamic conditions and TP concentration on the peak values of algal density in MLHR were explored. The main conclusions are summarized as follows:

  • (1) The algae density increased gradually from the upper to lower reaches along the MLHR. Compared with the scenario without WDP-TH, the peak values with WDP-TH construction at Xiangyang, Huangzhuang, Shayang, Qianjiang, and Xiantao increased by 2.7–26.9% in typical median water year and decreased by 21.2–45.9% in typical dry and extremely dry years. For both the scenario with WDP-TH and without WDP-TH, the peak values of algal density of each typical year did not exceed 107cell/L at Xiangyang, Huangzhuang, and Shayang sections, and would not result in algal blooms; algal blooms may occur at Qianjiang and Xiantao sections. The time of algal density exceeding 107cell/L was mainly found in mid-February or early March.

  • (2) The obvious negative correlation between peak algae density and flow rate was found and a positive correlation was evident between the algal density and TP concentration. However, under the combined impact of hydrodynamic and TP, TP concentration was not as important as that of hydrodynamic condition.

  • (3) The results provide valuable reference for diatom bloom control under different conditions. In the dry period of typical median water years, it is helpful to optimize the scheduling joint mode of WDP-TH and DJK, and increasing the flow rate of the water diversion can improve the hydrodynamic conditions, inhibit the growth of algae, prevent algae enrichment, and effectively reduce the probability of algal blooms occurrence. Meanwhile, it is an important measure to control diatom blooms in the long term by strengthening the pollution control of tributaries with large TP loads, and then reducing the TP concentration in the mainstream.

This work was supported financially by the National Natural Science Foundation of China (NO.52109005).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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