Influence of Salinity Gradient Changes on Phytoplankton Growth Caused

water

Article Inﬂuence of Salinity Gradient Changes on Phytoplankton Growth Caused by Sluice Construction in Yongjiang River Estuary Area

Menglin Yuan, Cuiling Jiang *, Xi Weng and Manxue Zhang

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China; [email protected] (M.Y.); [email protected] (X.W.); [email protected] (M.Z.) * Correspondence: [email protected]

Received: 15 August 2020; Accepted: 2 September 2020; Published: 7 September 2020

Abstract: Though the number of sluices and dams in coastal areas has increased rapidly in recent years, the influence of their construction on phytoplankton in estuary areas is hardly known. This paper aims to provide a reference for quantitative research on the ecological influence of sluice construction and give ecological justifications for the setting of environmental standards in the estuary areas. The survey data gained at the lower reach of the Yongjiang River and its estuarine areas in June 2015 were used in MIKE21 software (Danish Hydraulic Institute (DHI), Denmark)) for establishing a two-dimensional numerical model to simulate the salinity field distribution after sluice construction. Based on the simulation results, the salinity gradient changes caused by the construction were analyzed. The one-dimensional Gaussian model was applied to calculated the phytoplankton’s ecological threshold interval over the salinity changes, which helped predict the influence of salinity changes on phytoplankton cell density. The study shows that salinity in the Yongjiang estuary increases obviously, beyond the phytoplankton ecological threshold, after sluice construction without water discharge. Salinity will become a restriction factor to phytoplankton growth after sluice construction in the study area, which may cause a sharp decrease of certain phytoplankton species.

Keywords: estuary; ecological threshold; two-dimensional numerical model; sluice construction; salinity; Gaussian curve; phytoplankton

1. Introduction The increasing demand for oceanic resources and services from mankind has brought great pressure to the aquatic ecosystem [1,2]. Phytoplankton, as the most important primary producer in the aquatic ecosystem and a great participant in the global carbon cycle [1,3,4], is widely spread on the earth and plays a key role in energy flow, material circulation and information transmission [5]. Rojo C et al. studied phytoplankton species richness related to drought in the semiarid wetland of Las Tablas de Daimiel National Park in Central Spain and found out that phytoplankton could be used to effectively track the environmental changes in the wetland [6]. Rath A R et al. investigated the relationship between seasonal-spatial distribution of phytoplankton and environmental factors in the New Mangalore Port along the western coast in India. The authors found that chlorella and dinoflagellate’s high richness matched their high nutrition in monsoon seasons, which emphasized some diatom species’ potential as hydrology and water quality indicators in coastal ecosystems [7]. Several studies have analyzed the reaction of phytoplankton to ecological factors [8–11], but little analysis has been done from the perspective of an ecological threshold. Phytoplankton is very suitable for analyzing the ecological threshold of environmental gradients on large spatial scales because of its unique performance as an ecological indicator of environmental changes in different ecosystems [12].

Water 2020, 12, 2492; doi:10.3390/w12092492 www.mdpi.com/journal/water Water 2020, 12, 2492 2 of 15

Ecological Threshold (abbreviated as ET), refers to a certain point or interval, when the ecosystem rapidly transforms from one state to another [13]. Supposing that most species in the phytoplankton community can be served as indicators of environmental parameters, the concept of ET can be found as a community threshold. In this concept, community is defined as a whole in the changing environmental gradient, where internal structure changes are enhanced as a result of sharp changes in the abundances of several species [14,15]. It is very important to identify community thresholds in the gradients of human-influenced variables for predicting dramatic ecological changes and setting targets for restoration. Baker M E et al. applied a recent statistical method gradient forest to detect thresholds of phytoplankton community changes in French lakes. In the study, community thresholds were identified for the most important physico-chemical variables including water transparency, total phosphorus, ammonia, nitrates, and dissolved organic carbon to provide ecological justifications for the setting of environmental standards used in lake management [15]. More attention should be paid to the violent changes on the entire state of the community in the ecosystem [16]. In ecosystems, the transformation between two different states can be realized by a sudden or gradual transition. Thus, ET has two main types: ET point and ET zone. When the change of an ecological factor is close to an ET point, the ecosystem’s function, structure and procedure will change rapidly between two states [16,17]. The ecosystem will show two entirely different states around this critical point and cannot resume its initial state even if the environmental threat stops [18,19]. ET zone, unlike the ET point which transforms suddenly, refers to the process of an ecosystem’s gradual transformation between two states and it is more commonly found in natural ecosystems [20]. ET can be recognized both in time series analysis and in spatial comparison of environmental gradients [20–22]. ET studies have become a hot issue in recent years with the development of sustainability research [23–25]. The threshold analysis method is popular in evaluating the nonlinear response of environment changes. Its statistical performance plays a significant role in solving the prevalent environmental problems [26–28]. The ET phenomena of different ecological factors is widespread in various ecosystems, including that of the estuarine area. The estuarine area is a dynamic system for freshwater and marine ecosystem transition [29]. It provides important sources of nutrient substance and habitats for aquatic lives, closely linked to the environmental changes in coastal waters [3]. This paper focuses on the study in the Yongjiang River estuary in Ningbo, Zhejiang Province, China. A sluice construction is in the plan for the Yongjiang River estuary to meet urban development needs and to create greater socio-economic benefits. The Yongjiang estuary is a tidal reach, whose current velocity, water temperature, salinity and other factors are affected by river, wind, wave, and tide. Damming the river by a sluice construction will certainly change the natural hydrological environment in estuarine regions and influence phytoplankton growth in the aquatic ecosystem. This paper predicts and analyzes the influence of sluice construction in the Yongjiang River estuary on phytoplankton quantity from the angle of ET, using a two-dimensional numerical model established by MIKE21 software (DHI, Denmark) and a one-dimensional Gaussian model. Based on this, the impacts of the planned sluice construction on natural estuarine life resources are studied, strengthening the ecological researches on estuarine phytoplankton, augmenting the knowledge about estuarine ecosystem structure and improving the performance.

2. Materials and Methods

2.1. Description of Study Area Yongjiang River, referring to the reach below Sanjiangkou in Ningbo city, stretches 25.6 km long with its drainage area of 3857.2 km2 and a slope of 0.0117%. The Yongjiang estuary faces Huibie Sea at Hangzhou Bay in the north, Jintang and Zhoushan Islands in the east. Its southeastern sea is Luotou Waterway. With a total riverbank line of 60km, it extends from northwest to southeast. The study area is shown in Figure1. Water 2020, 20, x FOR PEER REVIEW 3 of 15

Yongjiang River, referring to the reach below Sanjiangkou in Ningbo city, stretches 25.6km long with its drainage area of 3857.2km2 and a slope of 0.0117‰. The Yongjiang estuary faces Huibie Sea at Hangzhou Bay in the north, Jintang and Zhoushan Islands in the east. Its southeastern sea is Luotou Waterway. With a total riverbank line of 60km, it extends from northwest to southeast. The study area is shown in Figure 1.

The Yongjiang basin has a subtropical humid monsoon climate with an obvious change between winter and summer monsoons. The wind direction is southeast in summer and northwest in winter. The Yongjiang tide is an irregular semi-diurnal tide, with an annual maximum tide level of 4.86 m, a minimumWater 2020, 12 of, 24920.13 m, an annual average high tide level of 3.14 m, a low level of 1.46 m and an average3 of 15 tide difference of 1.82 m.

FigureFigure 1. 1. TheThe study study area: area: The The lower lower reach reach below below the the Sanjiangkou Sanjiangkou in in Ningbo Ningbo is is the the Yongjiang Yongjiang River. River. TheThe Yongjiang Yongjiang estuary is north to Huibie Sea in Hangzhou Bay and east to Jintang Island.

2.2. DataThe Collection Yongjiang basin has a subtropical humid monsoon climate with an obvious change between winter and summer monsoons. The wind direction is southeast in summer and northwest in winter. Data of hydrology, water quality and biology of the study area involved in this paper were The Yongjiang tide is an irregular semi-diurnal tide, with an annual maximum tide level of 4.86 m, provided by Bureau of Hydrology and Water Resources Investigation at Lower Reaches of Yangtze a minimum of 0.13 m, an annual average high tide level of 3.14 m, a low level of 1.46 m and an average River, Hydrology Bureau of Yangtze River Water Conservancy Commission, and came from the tide diﬀerence of 1.82 m. measurements carried out in June 2015 at sampling locations presented in Figure 2.

2.2. DataIn Figure Collection 2, there were 45 sampling points in total, 12 in the lower reach of the Yongjiang River (withData number of hydrology,s 1 through water12). From quality these and 12, biology9 were taken of the from study three area cross involved-sections in, thissampled paper evenly were alongprovided the section by Bureau three of times Hydrology (e.g., 01~03, and Water marked Resources with “⊕ Investigation”). The other at 33 Lower points Reaches (with number of Yangtzes 13 throughRiver, Hydrology 45) were taken Bureau in 20 of locations Yangtze Riverin the Waterestuary, Conservancy with 13 locations Commission, sampled and in both came upper from and the lowemeasurementsr water (e.g. carried, 20/21, out marked in June with 2015 “ at⊕ sampling”) thus representing locations presented 26 points in. The Figure other2. single sampling locationIn Figures were2 marked, there were with 45“◯ sampling” in Figure points 2. in total, 12 in the lower reach of the Yongjiang River (with numbers 1 through 12). From these 12, 9 were taken from three cross-sections, sampled evenly along the section three times (e.g., 01~03, marked with “ ”). The other 33 points (with numbers 13 ⊕ through 45) were taken in 20 locations in the estuary, with 13 locations sampled in both upper and lower water (e.g., 20/21, marked with “ ”) thus representing 26 points. The other single sampling ⊕ locations were marked with “ ” in Figure2. After the phytoplankton samples# were collected, they were immediately fixed with Luge’s solution (usually 10 mL per liter of water). Then each sample was concentrated to 30~50 mL and poured into the calibrated precipitator. After 24~48 h, the supernatant was siphoned off. The sample could be stored in the sample bottle until it was concentrated to 30~50 mL. The types of samples were identified directly under the microscope and recorded. Their numbers were counted and calculated by microscope counting method. Water 2020, 12, 2492 4 of 15 Water 2020, 20, x FOR PEER REVIEW 4 of 15

FigureFigure 2. 2.Sampling Sampling locations locations in thein the lower lower reach reach and the and estuary the estuary of the Yongjiang of the Yongjiang River. The River. 26 locations The 26 accountlocations for account 45 points, for 1245 inpoints, the lower 12 in reachthe lower of the reach river of and the 33 river in the and estuary. 33 in the Of estuary. the 12 points, Of the nine12 points, were takennine were from taken three from cross-sections, three cross sampled-sections, evenlysampled along evenly the along section the three section times. three The times. other The 33 other points 33 (withpoints numbers (with numbers 13 through 13 through 45) were 45) taken were in 20taken locations in 20 inlocations the estuary, in the with estuary, 13 locations with 13 sampled locations in bothsampled upper in and both lower upper water. and Single lower sampling water. Single points sampling were marked points with were “ ” marked in their locations,with “◯ while” in their the multiple ones were marked with “ ”. ⊕ # locations, while the multiple ones ⊕were marked with “ ”. 2.3. ET and Gaussian Curve Theory After the phytoplankton samples were collected, they were immediately fixed with Luge's solutionMost (usually ET studies 10 mL focus per on liter the knownof water). single Then environmental each sample factorwas concentrated that can directly to 30~50 aﬀect mL aquatic and communitiespoured into the [15 ].calibrated The determination precipitator. of the After ET zone24~48 is h, inseparable the supernatant from Shelford’s was siphoned Law ofoff. tolerance The sample [30]. Incould Shelford’s be stored thought, in the sample an organism’s bottle until adaptation it was concentrated to various environmental to 30~50 mL. The factors types has of samples its ecological were minimumidentified anddirectly maximum, under the between microscope which and the organismrecorded. canThe survive.ir numbers The were organism’s counted function and calculated has its bestby microscope performance cou atnting or near method. the optimum point, fading towards both endpoints and ﬁnally suppressed at those endpoints. 2.3. ETThe and relationship Gaussian Curve between Theory organism and environmental factors is usually described by the bell-toleranceMost ET curve,studies matching focus on thethe concentration, known single symmetryenvironment andaleven factor variability that can direct of thely one-dimension affect aquatic Gaussiancommunit curve.ies [15]. Therefore, The determination this paper of simulated the ET zone the is above inseparable relationship from Shelford’s and presented Law anof tolerance ET zone range[30]. In by Shelford’susing a Gaussian thought, curve. an organism’s adaptation to various environmental factors has its ecologicalThe one-dimension minimum and Gaussian maximum model, between Equation which is: the organism can survive. The organism’s function has its best performance at or near the optimum1 point , fading towards both endpoints and y = c exp (x u)2/t2 (1) finally suppressed at those endpoints. −2 − InThe the relationship Equation, ybetweenrefers to organism one index and that environment represents phytoplanktonal factors is usually growth described condition; by thec is bell the- maximumtolerance curve value, match of correspondinging the concentration, index; u is symmetry the corresponding and even environmental variability of factorthe one value-dimension when itsGaussian biological curve. index Therefore, reaches thethis maximum paper simulated value; t meansthe above the relationship tolerance of theand index, presented which an describes ET zone biologicalrange by using species’ a Gaussian ecological curve. threshold. Generally speaking, one biological specie’s ET zone is (u 2t, u + 2t), and its optimum ET zone is (u t, u + t). Figure3 shows the Gaussian curve. − The one-dimension Gaussian model Equation− is:

1 = − ( − )/ (1) 2 In the Equation, refers to one index that represents phytoplankton growth condition; is the maximum value of corresponding index; is the corresponding environmental factor value when its biological index reaches the maximum value; means the tolerance of the index, which describes Water 2020, 20, x FOR PEER REVIEW 5 of 15

Waterbiological2020, 12 ,species’ 2492 ecological threshold. Generally speaking, one biological specie’s ET zone is5 of( 15− 2, + 2), and its optimum ET zone is ( − , + ). Figure 3 shows the Gaussian curve.

FigureFigure 3. 3.Gaussian Gaussian curve curve and and ecological ecological threshold: threshold: u u isis thethe correspondingcorresponding environmental environmental factor factor value value whenwhen the the corresponding corresponding biological biological indexindex reachesreaches thethe maximum,maximum, andand tt isis thethe tolerancetolerance ofof thethe index,index, whichwhich is is an an index index to to describe describe the the ecological ecological threshold threshold of of biological biologica species.l species. The The ecological ecological threshold threshold interval of a biological species is [u 2t, u + 2t], and the optimum ecological threshold interval is interval of a biological species is [u −− 2t, u + 2t], and the optimum ecological threshold interval is [u − [u t, u + t].The species can only survive in the survival range. t, −u + t].The species can only survive in the survival range. 2.4. Ecological Factor Selection 2.4. Ecological Factor Selection Ecological factor is the general term of various environmental factors that influence organisms. Ecological factor is the general term of various environmental factors that influence organisms. A large number of studies indicate that phytoplankton growth, its community structure changes A large number of studies indicate that phytoplankton growth, its community structure changes and and regional distribution are affected by many environmental factors such as dissolved oxygen, regional distribution are affected by many environmental factors such as dissolved oxygen, salinity, salinity, nutrient salts, water temperature, and PH value [31–35]. A Pearson Correlation Analysis nutrient salts, water temperature, and PH value [31–35]. A Pearson Correlation Analysis was made was made on the main environmental factors in the Yongjiang estuary. The result (see Table S1 in the on the main environmental factors in the Yongjiang estuary. The result (see Table S1 in the Supplementary Materials section) indicates that there is no obvious correlation between them despite Supplementary Materials section) indicates that there is no obvious correlation between them despite their respective influence on phytoplankton growth. In other words, their influence on phytoplankton their respective influence on phytoplankton growth. In other words, their influence on can be regarded as independent. Therefore, a preliminary study of phytoplankton ET under salinity phytoplankton can be regarded as independent. Therefore, a preliminary study of phytoplankton ET changes is undertaken in this paper. In most cases, salinity is a significant factor in the estuarine system under salinity changes is undertaken in this paper. In most cases, salinity is a significant factor in the controlling phytoplankton’s activity and diversity [36]. Sluice and dam construction will become estuarine system controlling phytoplankton’s activity and diversity [36]. Sluice and dam construction a physical barrier to nutrient transport and will inevitably change the original salinity field in the will become a physical barrier to nutrient transport and will inevitably change the original salinity estuary [10,36]. field in the estuary [10,36]. 2.5. Numerical Simulation by MIKE 21 Software (DHI, Denmark) 2.5. Numerical Simulation by MIKE 21 Software (DHI, Denmark) Two-dimensional and three-dimensional numerical simulations have been widely applied in hydrologyTwo-dimension and wateral quality and three simulations-dimension ofal rivers, numerical estuaries, simulations and oceans have [37been–43 ].widely MIKE applied 21(DHI, in Denmark)hydrology is and a professional water quality engineering simulations software of rivers, to simulate estuaries, hydrodynamic and oceans [37 force,–43]. waterMIKE quality, 21(DHI, sedimentDenmark) and is wave,a professional which has engineering advanced pre-processing software to simulate and post-processing hydrodynamic function force, andwater a friendly quality, customersediment pageand wave, and enjoyswhich has popularity advanced in pre simulating-processing water and post current,-processing wave, function sediment, and and a friendly other environmentalcustomer page variables and enjoys of river, popularity lake, estuary, in simulating bay, coast, water and ocean. current, wave, sediment, and other environmentThe followingal variables equations, of river, the conservationlake, estuary, ofbay, mass coast, and and momentum ocean. integrated over the vertical, describeThe the following flow and equations, water level the variations: conservation of mass and momentum integrated over the vertical, (1)describeContinuity the flow Equation and water of waterlevel variations: flow: (1) Continuity Equation of water flow: ∂z ∂M ∂N + + = 0 (2) ∂t ∂x ∂y Water 2020, 12, 2492 6 of 15

(2) Momentum Equations pf water ﬂow:

! ∂u ∂u ∂u ∂z u √u2 + v2 ∂2u ∂2u + u + v + g + g = vt + (3) ∂t ∂x ∂y ∂x c2h ∂x2 ∂y2 ! ∂v ∂v ∂v ∂z v √u2 + v2 ∂2v ∂2v + u + v + g + g = vt + (4) ∂t ∂x ∂y ∂y c2h ∂x2 ∂y2

In the Equation: x, y, and t are the space and time coordinates respectively; z is water level; u and v are the components of vertical mean velocity in the direction of x and y respectively; M and N are the components of discharge per unit width in the directions of x and y; M = hu, = hv; n is the Manning roughness coefficient; 1 1 6 c is the Chezy coefficient, c = n h ; vt is the turbulent viscosity coefficient; g is the gravitational acceleration.

2.5.1. Model Establishment and Parameter Calibration Simulation range covered the area from the Qingshuipu Bridge section along the Yongjiang trunk stream to the Zhoushan Bridge, Dong’ao oﬀshore. UTM-51 was chosen as the projection zone. A Flow Model (FM) triangular mesh was applied to divide the calculation area, forming 2422 mesh triangles and 1606 mesh vertexes. The bathymetric topographic map (1:2000) of Yongjiang estuary in June 2015 was used for interpolation calculation of meshes and for obtaining the bathymetric map of the model (FigureWater 42020). , 20, x FOR PEER REVIEW 7 of 15

FigureFigure 4. 4.The The bathymetric bathymetric map map of of thethe studystudy areaarea generatedgenerated by by MIKE MIKE 21 21 (DHI, (DHI, Denmark) Denmark).. Zhenhai, Zhenhai, Zhenhaikou,Zhenhaikou, and and Dong’ao Dong’ao are are the the observation observation stations.stations.

2.5.2.The Tide hydrodynamic Level Verification module was calculated by low order of accuracy, in which calculation time was from 08:00:00 16 June 2015 to 08:00:00 26 June 2015; time step was set to 3600s, CFL numbers of the The measured tide level values of the spring tide on 18 June, 2015 and neap tide on 24 June, 2015 shallowwere selected water Equation to verify and those the of transport the Zhenhai, Equation Zhenhaikou, were all and set Dong’ao to 0.8 to controlobservation equation stations convergence. (Figure The4). upstream boundary was measured by the water level at Qingshuipu Bridge in the same period,

2.5.3. Current Velocity Verification The measured current velocity of the neap tide on 24 June, 2015 was selected to verify the current velocity and flow direction at three observation points in the offshore area.

The offshore sea current compound is composed of the tidal current, wind current, and geostrophic current. It can be divided into periodic tidal current and non-periodic residual current. Therefore, the measured current velocity values could not be used for verification directly. A tidal current quasi-harmonic analysis according to short-term measured values was needed to predict tidal current values. The quasi-harmonic analysis uses a linear equation to analyze and calculate the northward and eastward components of tidal currents, solving tidal harmonic constants and residual current of partial tide by the least square method to present the north component u and the east component v at a designated time [44]. The harmonic constants of the tides selected by the quasi- harmonic analysis were used to forecast the flow corresponding to the simulation time for verification.

2.5.4. Sluice Construction Simulation There are studies related to sluice construction practicability and analysis of sluice site selection in Yongjiang already in China [45]. Based on them, this paper selected the ESTUARY GATE scheme. The upper boundary hydrological condition adopted the monthly lowest average flow of 90% assurance rate, 4.12m3/s, which was calculated by monthly average flow from 1985 to 2014 at the nearby Hongjiata station. The gate was set as a full water column considering the tide-resisting performance at the sluice site.

3. Results Water 2020, 12, 2492 7 of 15 and the downstream boundary was the deep sea. The Global Tide Prediction Model programmed in MIKE 21 (DHI, Denmark) was applied to create a tidal water level. After calibration, the value of eddy viscosity coefficient was set to 0.7 and manning coefficient was set to 60 m1/3/s. The Transport Module predefined in the software was selected to simulate salinity.

2.5.2. Tide Level Veriﬁcation The measured tide level values of the spring tide on 18 June 2015 and neap tide on 24 June 2015 were selected to verify those of the Zhenhai, Zhenhaikou, and Dong’ao observation stations (Figure4).

2.5.3. Current Velocity Verification The measured current velocity of the neap tide on 24 June 2015 was selected to verify the current velocity and flow direction at three observation points in the offshore area. The offshore sea current compound is composed of the tidal current, wind current, and geostrophic current. It can be divided into periodic tidal current and non-periodic residual current. Therefore, the measured current velocity values could not be used for verification directly. A tidal current quasi-harmonic analysis according to short-term measured values was needed to predict tidal current values. The quasi-harmonic analysis uses a linear equation to analyze and calculate the northward and eastward components of tidal currents, solving tidal harmonic constants and residual current of partial tide by the least square method to present the north component u and the east component v at a designated time [44]. The harmonic constants of the tides selected by the quasi-harmonic analysis were used to forecast the flow corresponding to the simulation time for verification.

2.5.4. Sluice Construction Simulation There are studies related to sluice construction practicability and analysis of sluice site selection in Yongjiang already in China [45]. Based on them, this paper selected the ESTUARY GATE scheme. The upper boundary hydrological condition adopted the monthly lowest average ﬂow of 90% assurance rate, 4.12 m3/s, which was calculated by monthly average ﬂow from 1985 to 2014 at the nearby Hongjiata station. The gate was set as a full water column considering the tide-resisting performance at the sluice site.

3. Results

3.1. Results of Sample Analysis After preliminary identiﬁcation, 53 kinds of phytoplankton were found in the study areas, in which there were 23 green algae covering 43.4% of total kinds, 12 blue-green algae covering 22.6%, and 11 diatoms covering 20.8%. The algae belonging to Bacillariophyta, Cyanophyta and Chlorophyta (e.g., Oscillaria sp, Melosira granulata, Tribonema Derbes et Solier, Microcystis aeruginisa, Pediastrum semplex) have the highest percentage in cell density.

3.2. Results of Water Level and Velocity Veriﬁcation

3.2.1. Results of Water Level Veriﬁcation After calculation, the result shows that the average relative errors between measured tide level and the simulated value at the three tide stations are within the range of 3.3–4.4% during the spring tide and between 2.4–3.8% during the neap tide, meeting the simulation requirements. Comparison between simulated water level and the measured water level is shown in Figure5. The simulated water level values are basically consistent with the measured water level values at each station. Water 2020, 20, x FOR PEER REVIEW 8 of 15

3.1. Results of Sample Analysis After preliminary identification, 53 kinds of phytoplankton were found in the study areas, in which there were 23 green algae covering 43.4% of total kinds, 12 blue-green algae covering 22.6%, and 11 diatoms covering 20.8%. The algae belonging to Bacillariophyta, Cyanophyta and Chlorophyta (e.g., Oscillaria sp, Melosira granulata, Tribonema Derbes et Solier, Microcystis aeruginisa, Pediastrum semplex) have the highest percentage in cell density.

3.2. Results of Water Level and Velocity Verification

3.2.1. Results of Water Level Verification After calculation, the result shows that the average relative errors between measured tide level and the simulated value at the three tide stations are within the range of 3.3–4.4% during the spring tide and between 2.4–3.8% during the neap tide, meeting the simulation requirements.

Comparison between simulated water level and the measured water level is shown in Figure 5.

WaterThe 2020simulated, 12, 2492 water level values are basically consistent with the measured water level values8 of 15at each station.

Figure 5. Tide Level Verification by comparison of simulated water level and the measured water level Figure 5. Tide Level Verification by comparison of simulated water level and the measured water values at the three observation stations: Zhenhai (a), Zhenhaikou (b), and Dong’ao (c). level values at the three observation stations: Zhenhai (a), Zhenhaikou (b), and Dong’ao (c). 3.2.2. Results of Velocity Verification Based on the quasi-harmonic analysis, tidal current matching the simulation time is predicted by harmonic constants of partial tide K1, M2, M3, M4, 2MK5, M6, 3MK7, M8. After calculation, the average relative errors between the simulated values and the measured values of flow velocity and direction at the three observation stations are within the range of 1.6–3.8% during the neap tide, which meet the simulation requirements.

3.3. Results of Salinity Simulation The average values of salinity during the simulation period in the estuary are introduced to Surfer15, interpolated by the Kriging method to draw contour plots. The two contour plots of salinity gradient changes before and after sluice construction are shown in Figure6. Contour map (a) was drawn with the measured data and contour map (b) was generated with the simulated data when a gate was added at the entrance to the estuary. The sampling points in the estuary (with number 13 through 45) were marked in both maps to analyze the salinity changes in these locations. Water 2020, 20, x FOR PEER REVIEW 9 of 15

3.2.2. Results of Velocity Verification Based on the quasi-harmonic analysis, tidal current matching the simulation time is predicted by harmonic constants of partial tide K1, M2, M3, M4, 2MK5, M6, 3MK7, M8.

After calculation, the average relative errors between the simulated values and the measured values of flow velocity and direction at the three observation stations are within the range of 1.6–3.8% during the neap tide, which meet the simulation requirements.

3.3. Results of Salinity Simulation The average values of salinity during the simulation period in the estuary are introduced to Surfer15, interpolated by the Kriging method to draw contour plots. The two contour plots of salinity gradient changes before and after sluice construction are shown in Figure 6. Contour map (a) was drawn with the measured data and contour map (b) was generated with the simulated data when a gate was added at the entrance to the estuary. The sampling points in the estuary (with number 13 Water 2020, 12, 2492 9 of 15 through 45) were marked in both maps to analyze the salinity changes in these locations.

Figure 6. Contour map of salinity: (a) Contour map of salinity drawn with the measured data; (b) Contour map of salinity generated with the simulated data when a gate was added at the entrance to the estuary. See Figure2 for detailed information about sampling locations and points.

3.4. ET Calculation Natural logarithms are taken on both sides of the Equation (1) to get the quadratic Equation (5), 2 where a = ln c u , a = u , a = 1 0 − 2t2 1 t2 2 − 2t2

F(x) = a0 + a1x + a2x (5)

The result of a quadratic ﬁtting of Equation (5) and the measured salinity values is: y = 0.432x2 + 17.92x 162 (R2 = 0.839). The ﬁtting curve is shown in Figure7. − − Water 2020, 20, x FOR PEER REVIEW 10 of 15

Figure 6. Contour map of salinity: (a) Contour map of salinity drawn with the measured data; (b) Contour map of salinity generated with the simulated data when a gate was added at the entrance to the estuary. See Figure 2 for detailed information about sampling locations and points.

3.4. ET Calculation Natural logarithms are taken on both sides of the Equation (1) to get the quadratic Equation (5), where = − , = , = −

() = + + (5) The result of a quadratic fitting of Equation (5) and the measured salinity values is: = −0.432 + 17.92 − 162 (R2 = 0.839). The fitting curve is shown in Figure 7.

Based on the transformation relationship between the fitted quadratic equation and Gaussian model, Gauss Equation is solved as:

1 () = 20.57 × 10 − （ − 20.91） /1.08 (6) 2

Water 2020According, 12, 2492 to the Equation, optimum value u is 20.91‰, ET zone is (18.75, 23.07) (‰), optimum10 of 15 ET zone is (19.83, 21.99) (‰).

Figure 7. Quadratic fitting of salinity: The quadratic curve y = 0.432x2 + 17.92x 162 fits well with Figure 7. Quadratic fitting of salinity: The quadratic curve = −−0.432 + 17.92 −− 162 fits well with the natural logarithm of measured phytoplankton cell density. R2 value is 0.839. the natural logarithm of measured phytoplankton cell density. R2 value is 0.839. Based on the transformation relationship between the fitted quadratic equation and Gaussian model,3.5. Descriptive Gauss Equation Analysis isof solvedSalinity as: Values Descriptive analysis of salinity values in 2422 computational meshes in the study area before 1 and after sluice constructionf (wasx) = conducted20.57 10.4 Texphe result(xs are20.91 shown)2/1.08 in 2Table 1. (6) × −2 − According to the Equation, optimum value u is 20.91%, ET zone is (18.75, 23.07) (%), optimum ET zone is (19.83, 21.99) (%).

3.5. Descriptive Analysis of Salinity Values

Descriptive analysis of salinity values in 2422 computational meshes in the study area before and after sluice construction was conducted. The results are shown in Table1. Table 1. Descriptive analysis of salinity (%).

Statistical Indicator No Gate Estuary Gate Minimum 13.776 15.239 Maximum 23.643 24.021 Mean 22.137 23.146 Median 22.256 23.179 Geometric Mean 21.970 23.102 Harmonic Mean 21.590 23.046 Root Mean Square 22.221 23.181 Trim Mean (10%) 22.351 23.292 Variance 3.744 1.622 Standard Deviation 1.935 1.273

Before sluice construction, the salinity range in Yongjiang oﬀshore sea water varied roughly from 21.7 to 23.6%, as shown in Figure6a, while the low salinity values were concentrated in the river channel area with a minimum salinity of 13.776%, as shown in Table1.

4. Discussion and Conclusions

4.1. Checking of Gaussian Curve Based on the ﬁtted one-dimension Gaussian Equation curve, a comparison can be drawn between measured phytoplankton cell density and Gaussian model theoretical values. As shown in Water 2020, 12, 2492 11 of 15

Figure8, the changing trend of the Gaussian model is consistent with that of the measured values, which suggests that the relationship between phytoplankton and salinity can be well described by the Gaussian Equation as calculated. Thus, the threshold zone (18.75, 23.07) (%) of salinity calculated in this paper is adequately representative of the appropriate salinity condition to which phytoplankton growth is subject in the Yongjiang estuary. Water 2020, 20, x FOR PEER REVIEW 12 of 15

FigureFigure 8. Gaussian 8. Gaussian curve curve and and observed observed values: values: The The Gaussian Gaussian curve curve drawn drawn according according to Equationto Equation (6) (6) ﬁttedfitted well well with with actual actual phytoplankton phytoplankton biomass biomass and and could could adequately adequately illustrate illustrate the the tolerance tolerance of of phytoplanktonphytoplankton to theto the salinity. salinity.

obtainedThe Gaussian in this Equationwork [47] also. Yi showsLi et al. its analyz reasonabilityed the correlation in the ET studiesbetween of L speciesabidocera in euchaeta other regions. and the Baoshansalinity Cui of etsea al. water adopted in theSuaeda same salsa area(L.) as Pall.Yangbiomass, finding as that an indexLabidocera to study euchaeta the ETET ofzone the Suaedaunder salinity salsa (L.)gPall.radientcommunity ranged (18.99,23.73 under the) scope (‰) and of changes optimum in salinity water depth ET zone and was soil (20.17,22.55) salinity in the (‰ Yellow) [48]. RiverThus, it Delta.can The be argued authors that applied applying a Gaussian the Gaussian model, which curve revealed for ET studies that the iswater effective depth to in quantify the ET zone biological of Suaedatolerance. salsa (L.) Pall. ranged ( 0.92, 0.08) (m), and that salinity in the ET zone ranged (5.17, 20.25) (g/kg). − Furthering this, a discussion was conducted around the potential impact of water–salt interaction on Suaeda4.2. Prediction salsa (L.) Pall. of Phytoplanktongrowth [46]. Density By taking the Gaussian model approach, Yiyi Yang et al. conducted a study on the relationship between phytoplankton cell density and various environmental factors at the After sluice construction, salinity in the estuary showed an increase since the gate acted as a Yueqing Bay located in Zhejiang Province, China, which is close to the Yongjiang River. It was found physical barrier to hinder the exchange between fresh water and sea water to some degree [10,36]. that the salinity in the ET zone fell within the range of (24.36,27.88) (%), with an optimum ET range According to the simulation, the maximum salinity reached 24.021‰, up from 23.643‰, while the of (25.24,27.00) (%), which is closely matched by the results obtained in this work [47]. Yi Li et al. average value surged from 22.137‰ to 23.146‰. On average, the level of salinity increased by about analyzed the correlation between Labidocera euchaeta and the salinity of sea water in the same area 1% in the entire estuary after sluice construction. At the sample point 12#, there was a notable increase as Yang, finding that Labidocera euchaeta ET zone under salinity gradient ranged (18.99,23.73) (%) in salinity, by around 0.4‰, which was close to where the planned sluice was located. For the sample and optimum salinity ET zone was (20.17,22.55) (%)[48]. Thus, it can be argued that applying the points 13–15# and 17–19#, which were near the Zhoushan Bridge, their salinity showed no notable Gaussian curve for ET studies is effective to quantify biological tolerance. increase, as it rose by merely 0.2‰. However, a high salinity region, covering sample points 16#, 28– 4.2.33#, Prediction 38–45#, of was Phytoplankton concentrated Density in the south of the Jintang Island, where the level of salinity increased by 0.4–0.8‰, as shown in Figure 6. After sluice construction, salinity in the estuary showed an increase since the gate acted as a physicalAccording barrier to to hinder the salinity the exchange the ET of between phytoplankton fresh water solved and by sea the water Gaussian to some curve, degree when [10 salini,36]. ty Accordingin the Yongjiang to the simulation, estuary falls themaximum within the salinity range reachedof 18.75~ 24.021%23.07‰, ,phytoplankton up from 23.643% survives, while. W thehen averagesalinity value ranges surged between from 19.83‰ 22.137% andto 23.146%21.99‰, .phytoplankton On average, the is levelleft in of the salinity optimum increased condition by about with a 1%higher in the entirephytoplankton estuary after cell sluicedensity construction.. After the sluice At the construction, sample point salinity 12#, there in the was estuary a notable is much increase higher than the threshold range suitable for phytoplankton growth. According to the Gaussian curve, the salinity range in the western sea area of the Jintang Island is 23.0 ± 0.6‰, which is beyond the optimum range for phytoplankton growth and is close to the lethal point. Moreover, the salinity value in the southern sea area of the Jintang Island reached 24.021‰, which is also beyond the lethal point, thus making it difficult for phytoplankton to grow in this area after sluice construction. As diatom, green algae and blue-green algae are the dominant groups in the phytoplankton community before sluice construction, it is expected that the abundance of freshwater green algae, which ranks as top in the phytoplankton community in the Yongjiang estuary, will be reduced significantly, while the brackish water algae (e.g., the diatom) may be subjected to only limited impact. Since the quantity of Water 2020, 12, 2492 12 of 15 in salinity, by around 0.4%, which was close to where the planned sluice was located. For the sample points 13–15# and 17–19#, which were near the Zhoushan Bridge, their salinity showed no notable increase, as it rose by merely 0.2%. However, a high salinity region, covering sample points 16#, 28–33#, 38–45#, was concentrated in the south of the Jintang Island, where the level of salinity increased by 0.4–0.8%, as shown in Figure6. According to the salinity the ET of phytoplankton solved by the Gaussian curve, when salinity in the Yongjiang estuary falls within the range of 18.75~23.07%, phytoplankton survives. When salinity ranges between 19.83% and 21.99%, phytoplankton is left in the optimum condition with a higher phytoplankton cell density. After the sluice construction, salinity in the estuary is much higher than the threshold range suitable for phytoplankton growth. According to the Gaussian curve, the salinity range in the western sea area of the Jintang Island is 23.0 0.6%, which is beyond the optimum range ± for phytoplankton growth and is close to the lethal point. Moreover, the salinity value in the southern sea area of the Jintang Island reached 24.021%, which is also beyond the lethal point, thus making it difficult for phytoplankton to grow in this area after sluice construction. As diatom, green algae and blue-green algae are the dominant groups in the phytoplankton community before sluice construction, it is expected that the abundance of freshwater green algae, which ranks as top in the phytoplankton community in the Yongjiang estuary, will be reduced significantly, while the brackish water algae (e.g., the diatom) may be subjected to only limited impact. Since the quantity of phytoplankton is an essential index for the evaluation of primary productivity, it is foreseeable that it will show a decreasing trend in the estuary after sluice construction.

4.3. Limitation and Prospect The ET of phytoplankton solved in this paper is limited to targeting the environmental gradient of salinity of the study area. There is the possibility that the sluice construction may affect other hydrological and nutritional factors to varying degrees, as a result of which the accuracy of the ET zone may be compromised to some extent. Besides, multiple sampling in a location is also a limitation imposed on this study. There may be autocorrelation biases of the sampling points on the same cross-section, as they are much closer to each other than the overall sampling points. The points sampled at different depths in the estuary are also the potential influencing factors for the results. The estuarine ecosystem is highly complex in structures, which makes it necessary to conduct more systematic and comprehensive studies on ET. Based on the results obtained in this work, the dominant phytoplankton species on different nutritional levels in the estuarine ecosystems should be monitored in future research. Besides, the number of samples should be increased and the sampling locations ought to be distributed in a more sensible way. More accurate indoor breeding experiments can be conducted to study the micro physiological response of phytoplankton to the water–salt gradient and its ET under other environmental gradients. Only in this way can the state of the ecosystem be better understood.

Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/12/9/2492/s1, Table S1: The Pearson correlation coefficient between ecological factors: salinity (Sal), dissolved oxygen (DO), water temperature (Temp), pH value (pH), and active phosphate concentration (PO4). Author Contributions: M.Y. finished the first draft of the paper and completed the calculation and verification of numerical models; C.J. edited and reviewed the manuscript; X.W. and M.Z. helped deal with the data. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors would like to thank the Bureau of Hydrology and Water Resources Investigation at Lower Reaches of Yangtze River, Hydrology Bureau of Yangtze River Water Conservancy Commission for providing the monitoring data used in this paper. Conflicts of Interest: The authors declare no conflict of interest. Water 2020, 12, 2492 13 of 15

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