In this paper, an extended three-dimensional (3D) model of the Yellow River Estuary (YRE) is established. It is based on a Delft 3D shallow-water high-resolution hydrodynamic model. The model is calibrated and verified by field observation data, which ensures its stability and accuracy, and provides a better simulation of the river plume in the estuary under different flow inputs. Since 2009, the spring flow pulse of the Yellow River can be divided into four types. The strong flow pulse is more conducive to the extension of the low salinity zone (LSZ). Doubling the peak discharge of pulse flow increases the LSZ area by 60%. The retardation time of salinity change in response to the flow pulse in estuary waters is weakly affected by the flow pulse intensity. The difference in shear front between the spring tide and the neap tide is the main dynamic mechanism contributing to the different retardation times. The average retardation time is 73 h during the spring tide and 55 h during the neap tide. In order to reduce the estuary salinity, it is suggested that the optimal time for the peak value of flow pulse in the river mouth is during the neap tide.

  • An extended three-dimensional (3D) model of the Yellow River is established based on a Delft 3D shallow-water high-resolution hydrodynamic model.

  • The study focused on the change process of salinity distribution in the Yellow River Estuary under different discharge conditions.

  • The retardation time of salinity change in response to the flow pulse in estuary waters is affected by the flow pulse intensity.

Fluvial discharge, wave energy, and tidal range are critical in determining the formation mechanism and development of river plumes (Alber 2002; Horner-Devine et al. 2009; Cheng et al. 2021). Of special concern is the significant changes in fluvial discharge and regional hydrodynamics in large worldwide estuaries because of climate changes and increasing human activities (Syvitski et al. 2005; Syvitski and Saito 2007; Murray et al. 2019). Global climate changes induce accelerated hydrosphere circulation, resulting in increased frequency of extreme events (e.g., super typhoons and floods) and possibly evoking hydrological and ecological influences at a broader scale (Intergovernmental Panel on Climate Change, IPCC 2013; Giosan et al. 2014; Liu et al. 2017). Human activities in terms of dam construction, water consumption, and land use changes exert increasing influences on hydrological, geomorphological, and ecological processes both in basin and delta systems (Nilsson et al. 2005; Feng et al. 2016; Jiang et al. 2018). For instance, dam operations regulate river discharge hydrographs by changing the timing, magnitude, and frequency of low and high flows, and disrupts water and sediment delivery (Graf 2006; Woodward et al. 2007; Kemp et al. 2016). At an estuary scale, large artificial reservoirs are able to alter regional hydrodynamics and biogenic elements, e.g., the Three Gorges Dam in the Yangtze River (Qiu et al. 2013; Li et al. 2016) and the Santo Antônio Dam in the Madeira River (Latrubesse et al. 2017). These examples demonstrate remarkable regional to global effects of water usage and hydrological regime changes, which merit specific examinations.

The Yellow River Estuary (YRE) is at the entrance of the Yellow River into the Bohai Sea. The freshwater from the Yellow River flows into the estuary and its adjacent sea, forming a low-salinity spawning ground and habitat environment (Liu et al. 2012; Song et al. 2019). The YRE has great practical significance and value in fishery resources, protection of biodiversity, and restoration of ecological function (Liu et al. 2012; Kuenzer et al. 2014). Affected by the dam reservoir, compared with the natural period, the inflow of the Yellow River into the sea is significantly reduced, especially in the spring of spawning and reproduction (Gu et al. 2019). It has a negative impact on fish spawning and reproduction. In recent years, the YRE has carried out ecological water scheduling and achieved remarkable results (Liu et al. 2020; Wu et al. 2020). For example, it is found that an impulse delivery of freshwater plays an important role in expanding the spawning habitat area of fish in the YRE (Wang et al. 2017; Yu et al. 2020). However, the efficiency of ecological water scheduling is very low, and the mode of scheduling is imprecise, especially the timing of flow pulses. One of the main difficulties and challenges is the lack of systematic understanding of the response effect of the water environment, including the low salinity zone (LSZ) distribution, to water resources scheduling. Therefore, by studying river plumes and salinity change processes in the YRE, this paper has developed a numerical predictive model with salinity as the main unknown variable. The present model is based on the shallow water high-resolution hydrodynamic model. The influence mechanism of the flow pulse on river plume in the estuary waters was retrieved through high resolution and accuracy of measured hydrodynamic and salinity data verification model of the YRE waters. In accordance with these results, the retardation time of salinity changes in response to flow pulse was proposed, providing a scientific basis for ecological water scheduling, estuarine ecological protection, and the development of offshore aquaculture in the Yellow River.

The Yellow River is the second largest river in China. Historically, lower reaches of the river have followed many different flow paths. Active estuarine delta flow paths have migrated from Shenshen River (1953–1964) and Diaokou River (1964–1976) to the present Qingshuigou (1976–present) (Ji et al. 2020). The most recent change was made artificially in 1996, causing the main channel to move northward from the Laoqingshuigou waterway to Qingbacha, forming the current YRE between Bohai Bay and Laizhou Bay (Fu et al. 2021). The estuarine delta generally refers to the fan-shaped area of about 5,500 km2 with Ninghai as the apex from Tuhai Estuary in the north and Zhimai Estuary in the south (Figure 1). The estuary area is rich in land resources, with the largest reserve resources and exploitation potential on the east coast of China. The explored reserves of petroleum, natural gas, brine, and other resources rank first in coastal zones in China. Approved by the State Council in October 1992, the delta reserve is a national nature reserve with a plentiful selection of both animal and plant resources. According to a preliminary investigation, there are 393 species of seed plants in the reserve, including 116 species of wild seed plants, 79 species of marine zooplankton, and 331 species of terrestrial animals (Jiang et al. 2013).
Figure 1

Location of the YRE.

Figure 1

Location of the YRE.

Close modal
The discharge from the Yellow River into the sea is the basis for the survival of animals and plants in the estuary area. It is especially relevant for the ‘Spring Flood’ in the Ningxia-Mongolia river section, an occurrence which brings abundant fresh water and nutrients into the estuary waters in spring and creates a freshwater environment for egg-laying and development of offspring. However, spring runoff of the Yellow River decreased significantly after 1973, and the ‘Spring Flood’ in the natural period once disappeared in the estuary. Figure 2 illustrates the spring flow in the Lijin station (generally regarded as the discharge of the Yellow River into the sea) over time since 1950. From Figure 2, it can be observed that the blue or dark blue discharge patches of flow have increased since 1973 and that the discharge into the sea has decreased significantly. Statistics have shown that the average runoff in the Lijin station during ‘Spring Flood’ in April from 1950 to 1972 was 2.868 billion m3, while during springs from 1994 to 2003, it was only 861 million m3. The latter number is less than 1/9 of the average runoff of 7.856 billion m3 from 1950 to 1972, accounting for only 17.18% of Sanmenxia runoff of 5.013 billion m3 in the same period.
Figure 2

Variation of spring flow in the Lijin hydrological station over time since 1950.

Figure 2

Variation of spring flow in the Lijin hydrological station over time since 1950.

Close modal

Calculation area and grid

For the study of hydrodynamic processes, predecessors have developed various mathematical models, for example, the finite element simulation for multiphase fluids (Lin & Jiang 2020), flow over ogee crested spillways, thin and sharp edges bodies–fluid interaction (Salih et al. 2019). However, most of these complex models remain at the conceptual stage and cannot be applied to the actual river. Previous studies have shown that the Delft 3D (three-dimensional) model using an orthogonal curve grid has strong advantages in the hydrodynamic evolution of estuaries and coasts (Dissanayake et al. 2012; van der Wegen & Roelvink 2012), which can realize the simulation and mechanism analysis of the evolution process of estuarine and coastal erosion and deposition at different time scales. The Delft 3D numerical model was adopted for the hydrodynamic calculation of salinity. The calculation area includes the offshore areas of the YRE, Bohai Bay, and Laizhou Bay, up to 121°E (Figure 3). To ensure the calculation accuracy and improve the calculation efficiency of the study area, the grid resolution was gradually changed from 15 m in the main river channel and 150 m in the estuary waters to 3,000 m near the open sea boundary, with a total of 384 × 194 grid points. The grid and shoreline in the offshore waters of the Yellow River Delta were well-fitted and the orthogonality and smoothness of the grids were also ensured. Furthermore, the accuracy and stability of the model calculation have been improved. The model is a 3D model with 11 σ layers in the vertical direction. Based on the Courant–Friedrichs–Lewy number (CFL number), a time step of 1 min was selected for the model.
Figure 3

(a) Model calculation area and grids used and (b) local grid amplification in the YRE with two 144 velocity observation points (Y1 and Y2).

Figure 3

(a) Model calculation area and grids used and (b) local grid amplification in the YRE with two 144 velocity observation points (Y1 and Y2).

Close modal

Settings of boundary conditions

The topography of the offshore waters and tidal flats of the Yellow River Delta was obtained by interpolation of the measured sections in 2015. Moreover, the topography of the main river channel and the tidal flat was obtained by interpolation of the measured large sections in October 2018. The open boundary of the open sea is driven by 13 main tidal components, namely M2, S2, K1, O1, N2, K2, P1, Q1, M4, MS4, MN4, MM, and MF. The data used for each tidal component are the result of the TPXO global ocean tidal wave model. The open boundary of the river is located 2.5 km upstream of dam No. 1 gauge station, and the dynamic conditions of the boundary were obtained by interpolating the daily discharge in the Lijin station. Driving model wind field data were gathered from ERA-interim reanalysis data of the European Centre for Medium-Range Weather Forecasts (ECMWF), which included meteorological and hydrological grid data every 6 h, i.e., wind speed and pressure at 10 m with a spatial resolution of 0.125° × 0.125° in the directions U and V.

Settings of important parameters

The bottom bed frictional force of the model was determined using Manning's coefficient. Manning's coefficient M is calculated using water depth (Xing et al. 2012):
formula
(1)
where ‘h’ denotes the water depth. In instances where h ≤ 1 m, M is 0.025.

Model verification

Data used for model verification were obtained by fixed hydrological observation near the YRE (the station location is shown in Figure 1). To fully develop the tidal wave from the open boundary of the open sea and the wave field in the study area, as well as to make the flow field more stable, the model simulation was started 30 days earlier than the observation time of the measured data. The calibration analysis of the hydrodynamic tidal effects system, flow velocity, and direction has been extensively verified. The results have shown that this model could well simulate the changes in hydrology, flow velocity, and flow direction. Verification of the model is described by Fan et al. (2020).

Data used for model salinity verification were obtained from fixed ship survey data carried out in the Yellow Estuary in August 2018. The location of the observation stations is shown in Figure 1. Moreover, Figure 4 demonstrates the point-to-point verification results of surface, middle, and bottom salinity observation data, and model-simulated salinity. Comparison between results obtained from the model and measured data showed that the model could accurately describe the characteristics of the surface and bottom diluted, as well as the scale of coastal extension of diluted water in the YRE.
Figure 4

Comparison of simulated and observed surface, middle, and bottom salinity of six fixed ship stations in the YRE.

Figure 4

Comparison of simulated and observed surface, middle, and bottom salinity of six fixed ship stations in the YRE.

Close modal
Figure 5 shows the verification results of modelled surface and bottom salinity compared to all measured data in the form of a Taylor diagram. The Taylor diagram shows the correlation coefficient, root mean square error, and standard deviation of data in the form of a polar coordinate system used to facilitate further error analysis (Taylor 2001). The Taylor chart can compare the relationship between various model indicators from multiple angles and dimensions so that readers can have a more intuitive understanding of the model's accuracy. As can be observed from Figure 5, the simulation accuracy of the model for bottom salinity is higher. In addition, the correlation coefficients verified by six stations are all above 0.8, while the root mean square error is less than 0.95. Although the simulation accuracy of surface salinity is not as good as that of bottom salinity due to a more complex and flexible structure of surface salinity, the correlation coefficient can still reach over 0.75, and the root mean square error is also below 0.98. These findings indicate that the model developed in this study is also able to accurately reflect real changes in surface salinity. In general, the model verification results of salinity simulation are good.
Figure 5

Taylor diagram of salinity verification results (the solid line, dotted line, and dashed line represent the standard deviation, correlation coefficient, and root mean square error coordinates, respectively).

Figure 5

Taylor diagram of salinity verification results (the solid line, dotted line, and dashed line represent the standard deviation, correlation coefficient, and root mean square error coordinates, respectively).

Close modal

Analysis of Yellow River spring flow pulse characteristics

It is generally assumed that a sudden fluctuation process of river flow in a short period of time can be seen as a flow pulse (Rolls 2021). However, there is neither a clear concept as to the extent of the short time and flow fluctuation, nor a definite identification method of the flow pulse. The continuous flow process can be regarded as a series of continuous time series signals, and the flow pulse can be taken to be the noise in a series of signals. The pulse signal can be found by finding the noise in the signal. The median filter has a good filtering effect on pulse noise and can well identify pulse signals (Wan et al. 2019). Therefore, this study adopted the median filter to remove signal noise and identify flow pulses in a flow time series.

In Figure 6, the original flow time process of the Yellow River from March to May in spring from 2009 to 2020 is illustrated by a blue curve, and the same process after a 20-order median filter is applied and is shown by the red curve. The area between these two curves is the flow pulse identified by the median filter. Using the median filter, a total of 34 flow pulses were identified during the spring periods of the years 2009 through 2020. These flow pulses can be classified according to pulse duration, peak discharge, and the difference between peak discharge and constant discharge (hereinafter referred to as discharge difference). Different levels of peak discharge were represented with points in different colors. Furthermore, the points were plotted in the coordinate system with duration as the horizontal axis and discharge difference as the vertical axis, as shown in Figure 7. It can be observed that the duration of these 34 flow pulses is mostly concentrated in 6–9 days, and the discharge difference is distributed from 30 to 300 m3/s. When the peak discharge is larger, the discharge difference is correspondingly larger, and the two achieve a positive correlation. Statistics also demonstrated that when the peak discharge was less than 300 m3/s, the discharge difference was mostly less than 100 m3/s. Additionally, when the peak discharge was 500–800 m3/s or greater than 800 m3/s, the discharge difference was mostly more than 200 m3/s. Finally, when the peak discharge was between 300 and 500 m3/s, the discharge difference was between 100 and 200 m3/s.
Figure 6

Original discharge and discharge after 20-order median filter from March to May in spring 2009–2020. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/hydro.2022.049.

Figure 6

Original discharge and discharge after 20-order median filter from March to May in spring 2009–2020. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/hydro.2022.049.

Close modal
Figure 7

Statistics and classification of the duration, peak discharge, and discharge difference of each flow pulse. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/hydro.2022.049.

Figure 7

Statistics and classification of the duration, peak discharge, and discharge difference of each flow pulse. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/hydro.2022.049.

Close modal

According to peak discharge and discharge differences, flow pulse processes lasting 6–9 days can be divided into four types. Three ellipses are shown in Figure 7. First, the green ellipse circles represented by all green points have a peak discharge of less than 300 m3/s, which can be regarded as Type 1. It was termed the Type 1 flow pulse to ease description and can also be referred to as the weak flow pulse. Second, the blue ellipse circles illustrated by all blue points have a peak discharge between 300 and 500 m3/s, which is also recognized as a specific flow pulse type. The flow pulse with a peak discharge between 300 and 500 m3/s and a discharge difference between 100 and 200 m3/s is referred to as the Type 2 flow pulse. Its intensity is greater than Type 1. There are two red points circled by the red ellipse. Furthermore, the peak discharge of the orange points is between 500 and 800 m3/s, and that of the red point is greater than 800 m3/s. They were divided into two types, respectively. More specifically, the flow pulse with a peak discharge between 500 and 800 m3/s and a discharge difference of more than 200 m3/s was termed the Type 3 flow pulse. Its intensity was greater than that of Type 2. The flow pulse with a peak discharge over 800 m3/s and a discharge difference over 200 m3/s was the strongest flow pulse and was named the Type 4 flow pulse.

River plume under different flow pulses

According to the characteristics of constant discharge, peak discharge, and discharge difference of the four types of flow pulses, a flow process with a time scale of 1 month (30 days) was designed to reflect the four types of different flow pulses. Figure 8 demonstrates the change rule of the flow process in this month. The first 11 days at the beginning of the month were constant discharge. The flow pulse was set in the middle of the month with a duration of 7 days, which started on the 12th day and reached the peak discharge on the 15th day. After 3 days of peak discharge, it decreased to constant discharge. Four types of flow processes were input into the extended mathematical model of diluted water, while all other settings of the model remained unchanged.
Figure 8

Four flow processes with four types of flow pulses.

Figure 8

Four flow processes with four types of flow pulses.

Close modal
The vertical average salinity distribution of the waters was calculated 12 days before and 22 days after the flow pulse input at 12 o'clock. The effects of flow pulses on the spatial distribution of salinity levels were compared and analyzed. In Experiment 1, the flow process containing the Type 1 flow pulse was considered. As can be observed from the results of Experiment 1 (Figure 9), the extension range of the diluted water before the flow pulse was limited was mainly concentrated near the mouth, and salinity concentrations near the mouth of the river were greatly reduced in response to a flow pulse from the river. The suitable salinity for spawning, hatching, and growth of fish and shrimp in this area should be about 25–29‰ (Li & Sheng 2011). So, we choose the area enveloped by 25, 27, and 29‰ isohaline to quantify the responses of the LSZ to different flow pulses. As shown in Table 1, all four pulses can increase the LSZ area, and the area increase of zone below 25‰ most significantly, more than 250%, followed by 27‰. Moreover, this increase is positively correlated with the pulse intensity. For example, the areas enveloped by 27‰ isohaline increase by 215, 230, 265, and 315% after the input of pulses 1–4.
Table 1

The changes in the LSZ area before and after four different types of flow pulses

ExperimentPeak discharge (m3/s)Area enveloped by 27‰ isohaline (km2)
Area enveloped by 27‰ isohaline (km2)
Area enveloped by 29‰ isohaline (km2)
Before pulseAfter pulseIncrease rate (%)Before pulseAfter pulseIncrease rate (%)Before pulseAfter pulseIncrease rate (%)
Experiment 1 230 52 182 250 87 274 215 196 458 134 
Experiment 2 345 154 563 266 164 541 230 282 772 174 
Experiment 3 526 184 725 294 238 868 206 321 948 195 
Experiment 4 818 202 943 367 284 1,180 315 434 1,582 265 
ExperimentPeak discharge (m3/s)Area enveloped by 27‰ isohaline (km2)
Area enveloped by 27‰ isohaline (km2)
Area enveloped by 29‰ isohaline (km2)
Before pulseAfter pulseIncrease rate (%)Before pulseAfter pulseIncrease rate (%)Before pulseAfter pulseIncrease rate (%)
Experiment 1 230 52 182 250 87 274 215 196 458 134 
Experiment 2 345 154 563 266 164 541 230 282 772 174 
Experiment 3 526 184 725 294 238 868 206 321 948 195 
Experiment 4 818 202 943 367 284 1,180 315 434 1,582 265 
Figure 9

Spatial variation of salinity in estuary waters before and after four different types of flow pulses.

Figure 9

Spatial variation of salinity in estuary waters before and after four different types of flow pulses.

Close modal
The peak discharge of the four tests can be compared in pairs, and the expansion times of the six peak discharge groups can be obtained. Comparing them with the increased rate of LSS, we can investigate the ability of different flow pulses. The scatter plot of the expansion times of peak discharge and the increase rate of the LSZ shows that there is a good linear relationship between them (Figure 10). Through the linear fitting formula, we found that when the peak discharge is expanded by 2 times, the areas enveloped by 25, 27, and 29 ‰ isohaline increase by 58, 58, and 63%, respectively. This fact shows that doubling the peak discharge of pulse flow increases the LSZ area by 60%.
Figure 10

The relationship between the expansion times of peak discharge and the increase rate of the LSZ.

Figure 10

The relationship between the expansion times of peak discharge and the increase rate of the LSZ.

Close modal

Retardation time of salinity change in response to the flow pulse

When river freshwater reaches the estuary, it always has a complex mixing effect with high-density seawater. Therefore, the arrival of pulse flow at the mouth cannot immediately increase the LSZ area. We define the time difference between the maximum flow and the maximum LSZ area as the retardation time of salinity change in response to the flow pulse. The calculation concept diagram of retardation time is shown in Figure 11. Count the change process of the LSZ area, calculate the time T2 when it reaches the maximum, and analyze the retardation time Tr in combination with the time T1 when the flow at the river mouth reaches the maximum. T1 and T2 can be calculated in each model test.
Figure 11

Conceptual diagram of salinity retardation time.

Figure 11

Conceptual diagram of salinity retardation time.

Close modal
Figure 12(a) shows the retardation time under various flow pulses in Experiments 1–4. Under the same flow pulse input, the retardation time of the three isohaline lines is different, among which the retardation time of 25‰ isohaline is the shortest, followed by 27 and 29‰ is the largest. We focus on the 27‰ isohaline for two reasons. First, water bodies with salinity less than 27‰ are most suitable for the reproduction and growth of fish in this area. Second, from the previous analysis, the area enveloped by 25, 27, and 29‰ isohaline changes is highly consistent, and the median value (27‰) can be taken as a representative. After different flow pulses are input, the retardation time is slightly different, but the difference is small. Taking the 27‰ isohaline line as an example, the maximum retardation time is 76 h, and the minimum is 68 h. The difference between the two is only 8 h, with an average of 73 h. This shows that the pulse intensity is not the main factor affecting the retardation time.
Figure 12

Retardation time under different flow pulses: (a) spring tide and (b) neap tide.

Figure 12

Retardation time under different flow pulses: (a) spring tide and (b) neap tide.

Close modal
The changes in the tidal level process show that it is a spring tide when the flow pulses enter the sea in Experiments 1–4, and the tidal range is about 1.2 m (Figure 13(a)). By the end of the month, the tide is converted to the neap tide on the 27th, and the area enveloped by 27‰ isohaline increases. This gives us enlightenment that the tidal changes have an impact on the retardation time. Will the lag time be different if the sea is in a neap tide when the peak discharge reaches the river mouth? For this problem, we have conducted four groups of experiments. In Experiments 5–8, only the open boundary of the outer sea is changed, so that the sea area is neap tide when the peak discharge reaches the river mouth. The flow pulse used in Experiments 5–8 is the same as that in Experiments 1–4. The calculated retardation time is shown in Figure 12(b). It can be seen that during the neap tide, the maximum retardation time is 59 h and the minimum is 51 h, with an average of 55 h (Figure 12(b1)12(b4)). Only by changing the tidal condition, different retardation times are obtained under the same flow pulse. This shows that the tidal condition is an important factor affecting the retardation time of salinity change.
Figure 13

Flow and salinity changes in Experiments 1–4. (a) Tidal process in the estuary (b1, b2, b3, and b4) flow process at the river mouth from model and area change process enveloped by 27‰ isohaline.

Figure 13

Flow and salinity changes in Experiments 1–4. (a) Tidal process in the estuary (b1, b2, b3, and b4) flow process at the river mouth from model and area change process enveloped by 27‰ isohaline.

Close modal

The dynamic mechanism of retardation time difference

The shear front in the YRE waters and low-velocity zone play an important role in substance transport from the estuary into the sea. They effectively block the estuary fluctuated tidal water transport and sediment transport to the sea, which is the ‘catcher’ of substance in the estuary, and the estuarine diluted water body and high sand suspended center mainly limited to the landward side of the shear front (Li et al. 2001; Wang et al. 2017). Based on the diluted water diffusion model, we found that during the spring tide, both the shear front of inward flooding and outward ebbing type (IFOE) and the shear front of inward ebbing and outward flooding type (IEOF) are relatively completely developed in the YRE. During the neap tide, the IEOF almost did not develop with no effective shear front zone being formed.

In order to quantify the difference between the shear front during spring and tide, we calculated the intensity of the shear front. The shear strength could be defined according to the difference between the low-velocity zone and the velocity on both sides. The greater the difference of the shear front, the stronger the difference of the shear front. The shear strength of the tidal shear front is defined as the average difference between the centerline of ‘Gc’ (gradient of current) front and the velocity on both sides:
formula
(2)
where v and v′ are the velocity of two points perpendicular to the front on both sides of the front, respectively; d is the distance between the two points; and v0 is the velocity at the center of the shear front. According to calculation, in the middle of spring and neap tide (within 1 day), the average intensity of shear front during the spring tide is 0.041 m/s/km, while that during the neap tide is only 0.036 m/s/km. There is little difference between the two, but the former exists for 10.22 h, while the latter exists for only 5.21 h. The gray shading in Figure 14 is the time when the shear front exists. The difference in flow velocity at points Y1 and Y2 (on the inner and outer sides of the shear front, respectively, see Figure 3 for their locations) can confirm the existence time of the shear front. It can be seen that the blocking strength of the shear front during the spring tide is twice that during the neap tide.
Figure 14

The flow velocity process of characteristic points on both sides of the shear front, the time period of the shear front (gray shaded area) and the shear front intensity. (a) During the spring tide and (b) during the neap tide, within 1 day at the middle time of the spring tide.

Figure 14

The flow velocity process of characteristic points on both sides of the shear front, the time period of the shear front (gray shaded area) and the shear front intensity. (a) During the spring tide and (b) during the neap tide, within 1 day at the middle time of the spring tide.

Close modal

During the spring tide, the strong shear front effectively blocked the mixing between the freshwater and seawater, in turn affecting the diffusion rate of diluted water, and naturally increasing the retardation time of the salinity change in response to a flow change. In line with the mixing of freshwater and seawater, the barrier effect of the incomplete and weak shear front during the neap tide is weakened. Accordingly, the retardation time of salinity change in response to flow change is smaller than that during the spring tide. The difference between the shear front in the estuary area during the spring tide and neap tide is the main dynamic mechanism behind the retardation time difference.

Ecological water scheduling suggestions in spring estuary

The LSZ area increase after the input of flow pulses during the spring tide (tests 1–4) and neap tide (tests 5–8) are counted, respectively. Table 2 shows the statistical results under the four types of flow pulses during the neap tide. The extension area multiples of 25‰ isohaline, 27‰ isohaline, and 29‰ isohaline envelope after the flow pulse are greater than during the spring tide. Thus, compared with the spring tide, the not-developed shear front is also conducive to the extension of the diluted water during the neap tide. In addition, the LSZ is distributed over a larger area. The aforementioned results demonstrate that the LSZ distribution can effectively be increased if the timing of the flow pulse into the sea is properly grasped in the water scheduling practice of the YRE. In other words, the peak discharge entering the sea at a neap tide can effectively increase the LSZ distribution.

Table 2

Four types of flow pulses and the changes in the isohaline area before and after entering the sea

Pulse typeExtension times of area enveloped by 25‰ isohaline
Extension times of area enveloped by 27‰ isohaline
Extension times of area enveloped by 29‰ isohaline
Spring tide periodNeap tide periodSpring tide periodNeap tide periodSpring tide periodNeap tide period
Pulse 1 3.5 3.7 3.1 3.4 2.3 2.4 
Pulse 2 3.7 3.9 3.3 3.5 2.7 2.9 
Pulse 3 3.9 4.3 3.6 3.9 3.0 3.2 
Pulse 4 4.7 5.1 4.2 4.6 3.6 3.8 
Pulse typeExtension times of area enveloped by 25‰ isohaline
Extension times of area enveloped by 27‰ isohaline
Extension times of area enveloped by 29‰ isohaline
Spring tide periodNeap tide periodSpring tide periodNeap tide periodSpring tide periodNeap tide period
Pulse 1 3.5 3.7 3.1 3.4 2.3 2.4 
Pulse 2 3.7 3.9 3.3 3.5 2.7 2.9 
Pulse 3 3.9 4.3 3.6 3.9 3.0 3.2 
Pulse 4 4.7 5.1 4.2 4.6 3.6 3.8 

The research object of this study was the YRE and its adjacent waters. The study focused on the change process and law of salinity distribution in the YRE under different discharge conditions by means of historical data analysis, field investigation, numerical simulation, and other methods. Through eight separate experiments, substantial progress has been obtained.

  • Based on the shallow water high-resolution hydrodynamic model, the extended 3D model of diluted water of the Yellow River was established. Moreover, the ECMWF wind field-driven surface wave field scheme was coupled in the Delft 3D hydrodynamic numerical model to better describe the characteristics of residual currents in the sea. Calibration and verification of the model were performed using field observation data, which ensured the stability and accuracy of the model and realized the simulation of diluted water of the Yellow River under different flow inputs.

  • Based on the principle of the median filter for noise removal, the flow pulse process of the Yellow River from March to May in spring since 2009 was automatically identified. According to the pulse duration, the peak discharge, and the discharge difference between peak discharge and constant flow, the Yellow River spring flow pulse since 2009 may be divided into four types. The flow pulse with a peak discharge greater than 800 m3/s and a discharge difference greater than 200 m3/s is the strongest. The duration of the flow pulse is mainly concentrated in a 6- to 9-day period, and the discharge difference is distributed from 30 to 300 m3/s. When the peak discharge is larger, the discharge difference is correspondingly larger, and the two achieve a positive correlation.

  • The response of salinity distribution in estuary waters varies with different types of flow pulse. The strong flow pulse is more conducive to the LSZ extension. It plays an important role in the growth and reproduction of organisms in estuary waters. Doubling the peak discharge of pulse flow increases the LSZ area by 60%. The retardation time of the water's salinity changes in response to the flow pulse in the estuary is weakly affected by flow pulse intensity. The difference between the shear front in the YRE during the spring and neap tide is the main dynamic mechanism triggering the retardation time difference. The average retardation time of the estuary is 73 h during spring tide and 55 h during neap tide.

  • The extension rate of the LSZ formed by flow pulses during the neap tide is higher than those during the spring tide. It is suggested that the optimal time for the peak value of flow pulse in the river mouth is during the neap tide. In future research, it will also be necessary to focus on the actual situation of ecological elements such as individual density, species diversity, and biomass to explore the optimal estuarine ecological scheduling scheme.

This research was funded by the National Natural Science Foundation of China (No. U2243207, No. 52079056, and No. 41976187), Basic Scientific Research Project of YRIHR (No.HKY-JBYW-2020-06 and No.HKY-JBYW-2020-11), and the National Natural Science Foundation of Shandong Province (No. ZR2020MD063).

All data are included in the paper or available on request from the corresponding author.

The authors declare there is no conflict.

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