Simulation study on the impact of ecological water recharge on the riverine environment based on Mike21

doi:10.21203/rs.3.rs-3226164/v1

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Simulation study on the impact of ecological water recharge on the riverine environment based on Mike21

https://doi.org/10.21203/rs.3.rs-3226164/v1

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Through the simulation study of river ecological recharge, it reveals the law of water quality change under different ecological recharge scenarios of the White River, and provides theoretical basis for optimizing the recharge scheme and improving the recharge effect. Based on Mike21, a water environment model for ecological recharge of the river was established to simulate the evolution of DO,COD_Mn, COD, NH₃-N and TP in the Baihe River under different scenarios, to analyze the water quality characteristics of the river, to evaluate the advantages and disadvantages of different recharge strategies, to explore the optimization scheme of ecological recharge of the Baihe River, and to analyze the drivers of the impact on the water environment. The results show: Among the three recharge scenarios, the scenario with 2 times the flow rate and 1/2 the recharge duration had the best recharge effect, and the river was upgraded from Class IV water quality standard to Class III water standard; The White River has better water quality conditions under non-flood conditions because it is less subject to confluence, while flood conditions tend to have poorer water quality conditions due to the effects of multiple sources of pollutant pooling; Water quality conditions during the recharge period of the river were better than during the non-recharge period, and water quality indicators showed a spatial pattern of upstream being better than downstream.

Ecological recharge

Numerical analysis

Mike21

Aquatic ecology

The White River

Ecological recharge is an effort to provide additional water to damaged ecosystems to meet their minimum ecological water needs through engineered or non-engineered means (Zhang et al.2022). Compared with traditional management methods, ecological water replenishment not only has economic and social benefits, but also has positive significance on environmental protection, resource utilization and human health (Xu and Lin 2021;Yang et al.2022). Ecological recharge enhances the self-purification capacity of water bodies and reduces pollutant concentrations. Moreover, it plays a vital role in enhancing the restoration of local ecosystems. In addition, ecological water recharge is increasingly used in urban planning because it improves local environmental conditions and soil quality, among other things.Therefore, domestic and foreign scholars' research on ecological water replenishment methods and effects has become the focus and hotspot.

Ecological water replenishment is a common domestic and international measure for comprehensive river training, which has the function of restoring the ecosystem function of the receiving water area and realizing the ecological dynamic balance of the receiving water area. In order to manage the water environment of rivers in England and Wales, Geoffrey E P proposed an "ecologically acceptable" flow calculation method, which takes into account a number of factors such as river connectivity, vertical exchange, diffuse flow and minimum flow(Geoffrey.2016). Gleick P H introduced the concept of ecological water demand, which refers to the amount of water resources required by river ecosystems to safeguard their functional and structural integrity and to maintain biodiversity(Gleick et al.2023). In order to restore the Sudanese wetland ecosystem that was severely affected by the irrigation project, Dadaser-Celik F et al. developed a cost-effective ecological recharge program by estimating the total cost as well as the marginal benefits required for water transfer(Dadaser et al.2019). Baulle-Aguilar J et al. conducted experiments on seepage from a river after ecological recharge and used the HydroGeosphere model to develop a two-dimensional coupled surface water and groundwater model(Jordi et al.2015). In recent years, with the successful implementation of large-scale water transfer projects such as South-to-North Water Diversion, Diversion of River to Han and Diversion of Yellow River to Hebei, ecological water replenishment has gradually attracted extensive attention from domestic experts and scholars. In order to explore the optimal configuration of the river ecological recharge program, Wang Feifei et al. established a two-dimensional numerical model based on MIKE21 for the heavily polluted river Furniu Creek in Chongqing, and the simulation results showed that the two-point recharge program is economically feasible and has a high rate of water quality improvement (Wang et al.2021). Sun Ran et al. established a hydrodynamic model for the Beijing section of Yongding River, simulated and analyzed the two recharge projects of Guanting Reservoir on Yongding River, determined the reasonable interval of the recharge flow rate through the simulation results, and put forward the ecological benefit optimization scheme of recharging with alternating flow rate size(Sun et al.2021). Song Bing et al. sampled the water quality of the receiving rivers of the water diversion project, constructed a comprehensive evaluation model of water quality based on the information entropy theory, and analyzed that ecological water replenishment is carried out in summer, and the effect of water quality improvement is obvious(Song ,2021).

2.1 Geographical location

Baihe River, a first-class tributary of the Han River, with its source located in the Huangshiya area of the Funiu Mountains, is the right main tributary of the Tangbai River. Its origin is located in Nanzhao County, Henan Province, and flows along the northwest to southeast direction through Wancheng District, Wolong District, Xinye County, etc., and finally joins with the Tang River to form the Tangbai River in Xiangyang City, Hubei Province, and eventually injects into the Han River (Wang ,2022). The White River has a total length of about 264 kilometers and a watershed area of 12,270 square kilometers, covering a range of geographic coordinates between 112°20' and 112°27' east longitude and 32°10' and 33°25' north latitude. The upper tributaries of the Baihe River mainly include the Yellow Duck River and Duck River systems; and the middle and lower reaches include the Sishui River and the Turbulent River tributaries (Zhang et al.2022). These tributaries provide the Baihe River with sufficient water and rich ecological resources from different directions, and play an important role in promoting the development and utilization of local water resources and ecological environmental protection. In addition, many historical and cultural heritages are preserved in the Baihe River Basin, such as the Yangjialing Burial Group in the Tang Dynasty and the Ancient Weir Group in the Song Dynasty, which bear witness to the long history and cultural heritage of the area (Li ,2021).

In summary, as an important tributary of the Han River, the Baihe River has irreplaceable importance and value in the development of local natural resources, ecological protection and historical and cultural heritage. In the future, it is necessary to further strengthen its ecological environmental protection and resource utilization management to promote its sustainable development and local socio-economic prosperity.A map of the administrative division of Nanyang City, to which Baihe belongs, is shown in Fig. 2 − 1.

2.2 Recharge situation

In 2020, the South-to-North Water Diversion Project implemented ecological water replenishment to the Baihe River, and its effect was remarkable. After 44 days of water replenishment, the total amount of water reached 85.07 million cubic meters, and the use of high-quality water from Danjiangkou Reservoir, which made the water quality of Baihe River steadily improve from the original Ⅳ water quality to the Ⅲ water standard of the functional area. This result is of great significance for local ecological environment protection and ecological resources development.

In conducting the simulation scenarios, the researchers chose the Class IV water quality standard of the White River before water recharge in 2020 as the initial condition. This selection was based on the actual situation, as the White River was subjected to a large number of pollutants and other external factors prior to recharge, leading to a further deterioration of its already serious pollution situation. The use of high-quality source supply from the Danjiangkou Reservoir, together with a series of measures such as meticulous and scientific planning, has led to rapid improvement of the ecological environment in the Baihe River basin and promoted local socio-economic prosperity. It is worth noting that in the actual implementation process, the South-to-North Water Diversion Project does not simply inject water resources directly into the White River. Instead, the recharged water is subjected to a series of purification and treatment processes to ensure that the quality of the recharged water is stabilized above the Class II water standard.

In summary, the implementation of the ecological water recharge program to the Baihe River by the South-to-North Water Diversion Project is a successful practice. This program has made an important contribution to solving the local water shortage problem, improving the ecological environment and promoting sustainable development, and also provides a useful reference for resource management and protection in other basins.

3.1 Model Establishment

(1) Scope of the calculation

This simulated section of the Baihe River is located near the downtown area of Nanyang City, with the upstream boundary being the section of the Baihe River at the Shangzhantou barrage and the downstream boundary being the section of the Baihe River at Shangfanying in Nanyang. It is located between longitude 112°28'E and 112°37'E and latitude 33°40'N and 32°54'N. The length of the simulated river section is 35.46km, and the total area is 22.06km2. This study adopts the Mercator (UTM) projection, and we know that the study area is in the 49th zone of the Mercator projection UTM zones by the projection transformation of latitude and longitude, so we choose WGS_1984_UTM_ZONE_49N to be used. 1984_UTM_ZONE_49N was chosen as the base coordinate system for this study. The simulated river reach is shown in Fig. 3 − 1.

(2) Grid division

The Mike FM module is a powerful tool for generating various types of meshes. One of them is the quadrilateral mesh known as structured mesh, which is characterized by defining nodes on each layer of the grid lines and an equal number of nodes on each layer. This type of mesh is suitable for objects with regular shapes and simple structures because it has a more organized and regular layout. In a triangular mesh, the points inside the mesh do not have the same adjoining cells, which makes it applicable to a wider range of objects and can be adjusted more easily. Triangular meshes generate better quality meshes with high accuracy. Therefore, in this study, we chose to use the triangular unstructured mesh form for simulation and analysis (Zhang et al.2022;Yang,2020). By using the Mike FM module to generate triangular and quadrilateral meshes, we can choose the most suitable mesh type according to the specific needs and characteristics of the object. Whether you need a structured, regular mesh or a more free and flexible unstructured mesh, the Mike FM module provides high quality, accurate meshes that provide a reliable basis for research and analysis work.The effect of the grid generated according to the river topography data is shown in Fig. 3 − 2.

(3) Grid optimization

The LOP mesh local optimization algorithm is a method used to optimize a triangular mesh by expanding the minimum angle of the mesh by processing the diagonals of the quadrilateral formed by adjacent triangles, thus making the mesh more homogeneous, stable, and easy to compute (Zhang et al.2022). Specifically, the algorithm first finds all the triangles that do not satisfy the minimum angle requirement and splits them into several smaller triangles. Next, the newly generated triangles are inserted into the original mesh while keeping the original topology intact. In this way, a more homogeneous and rational mesh is obtained. To further improve the effectiveness of the algorithm, some extensions can be used. For example, when dealing with neighboring triangles, some smoothing factors can be introduced to avoid over subdivision or over weakening of the original structure. In addition, some special cases, such as sharp corners and overhanging points, need to be considered in the whole process and appropriate measures need to be taken to solve the problems. In conclusion, LOP algorithm is a very practical and effective optimization method, which is widely used in computer graphics, engineering design, geological exploration and other fields.The mesh optimization process is shown in Fig. <link rid="fig2">3</link>–3.

3.2 Parameter selection and ratemaking

The table of water quality model parameters obtained by reviewing related literature is shown in Table 3 − 2 (Rehanguri,2022), which gives the range of parameter values under different conditions, and the default values are used if they are not labeled in the table.

Table 3

− 2 Water quality model parameter table
State Variable	Parameter	Value	Unit
COD	20℃ COD degradation rate coefficient	0.005	/day
	Temperature coefficient of COD degradation rate	1.05	dimensionless
	COD adsorption coefficient	0.004	/day
	Semi saturated oxygen concentration	2	mg/L
COD_Mn	First order degradation rate of dissolved state	0.3	/day
	Temperature coefficient of degradation in dissolved state	1.08	/day
	Oxygen concentration semi saturation number	2	mg/L
	Ratio coefficient of ammonia release during COD degradation	0.3	gNH₄/g COD
	BOD attenuation release ammonia ratio	0.3	gNH₃-N/g BOD
NH₃-N	Plant nitrogen absorption	0.06	gN/g DO
	Nitrogen absorption by bacteria	0.1	gN/g DO
	Nitrogen uptake half saturation	0.05	mg/L
	Semi saturated oxygen concentration of Nitrification	2	mg/L
	Temperature System of Attenuation Rate of Ammonia Conversion to Nitrite	1.1	dimensionless
DO	Maximum oxygen production rate at noon (/m²)	2	/day
	Plant respiration rate (/m²)	0.115	/day
	Oxygen demand of sediment (/m²)	0.55	/day
	Oxygen demand temperature coefficient of sediment	1	/day
	Aerobic semi saturation number of sediment	2	/day
	DO saturation concentration	Temperature dependent

3.3 Water quality model validation

This study addresses the field of water environment simulation with 2020 as the status quo level year. By comparing the differences between water quality data from actual monitoring stations and model predictions, the optimal parameter combinations were determined and applied to subsequent water quality simulations (Lei et al.2021). In the further study, we will pay attention to the water quality indicators at the Shangfanying section in Nanyang, and verify the accuracy and reliability of the established water environment model by comparing the simulation results with the actual monitoring data on a month-by-month basis. Specifically, we will select the year 2020 as the evaluation base year and carry out systematic monitoring in that year, and analyze the degree of influence by combining meteorological, hydrological, environmental and other factors.

In addition, some possible error factors need to be considered in the evaluation process, and corresponding measures should be taken to control and correct them. Before the comparative analysis, the simulation of the day-by-day water quality data to take the monthly average value, will be converted into a monthly average water quality series, the simulated value of each water quality indicator and the observed value of the comparison is shown in Fig. 3–5.

According to the results in Figs. 3–5, this simulation is good and accurately reflects the trend of each pollutant indicator. Among them, the overall fluctuations of DO, COD and CODMn are more obvious, showing lower levels before and after the flood season and during the dry period time. NH3-N and TP, on the other hand, are one of the important indicators of eutrophication degree of water bodies. By analyzing the simulation results, we found that these two indicators also have periodic fluctuations, and their trends are similar to other indicators. Specifically, the NH3-N and TP content showed an increasing trend before and after the flood season as well as during the dry season, while it was relatively stable in the non-flood season. During the winter period, NH₃-N and TP are at a relatively high level due to the lowering of the temperature of the river water bodies, the decrease in the uptake of the chemical elements by aquatic plants, animals and microorganisms in the water bodies, the limitation of self-purification of the water bodies, and the lowering of the reduction of pollutant concentrations in the water bodies during the period.

In summary, the results of this simulation effectively reflect the patterns of different pollutant indicators in the region over time and reveal their complex relationships with seasonal, hydrological and other factors. Based on this information, we can formulate more refined and targeted environmental protection policies and implement relevant measures to improve the quality of the local water environment.

In order to better describe the accuracy of the model, the deterministic coefficient R-square is introduced to further evaluate the fit of the model, and the calculation formula can be expressed as:

$$SSR={\sum }_{i=1}^{n}{w}_{i}{\left({\widehat{y}}_{i}-{\stackrel{̄}{y}}_{i}\right)}^{2}$$

$$SST={\sum }_{i=1}^{n}{w}_{i}{\left({y}_{i}-{\stackrel{̄}{y}}_{i}\right)}^{2}$$

$$R\text{-square }=\frac{SSR}{SST}$$

where ${\widehat{y}}_{i}$ denotes the simulated value, ${\stackrel{̄}{y}}_{i}$ denotes the mean value of the observed data, and ${y}_{i}$ denotes the observed data.

After the above formula to calculate the certainty coefficient of each water quality indicator, the results are shown in Table <link rid="tb3">3</link>–3. The coefficient of determination provides a good description of the goodness of fit of the simulation results; in general, a value of R-square greater than 0.8 is considered to be a good fit, and a value of R-square greater than or equal to 0.9 is considered to be a good fit. The five water quality indicators in this simulation are all greater than 0.8, and the best fit is COD, with a coefficient of determination of 0.95. The fit of this simulation meets the demand and can accurately restore the spatial and temporal evolution of the water quality of the White River.

Table 3

3 Deterministic coefficients of each water quality index of the Bai River
	DO	COD	COD_Mn	NH₃-N	TP
R-square	0.84	0.93	0.94	0.91	0.90

3.4 Scenario setting

(i) Selection of a typical representative year

After the implementation of ecological water replenishment to the White River by the South-to-North Water Diversion Project in 2020, the water quality will be stabilized to Class III water standard and reach the water quality target of the functional area. After reviewing the relevant information(Xu et al.2021), the water quality of the White River before the replenishment in 2020 will be Class IV water quality, so the water quality standard of Class IV will be used as the initial water quality condition for the simulation scenario of the replenishment period of the White River. Hydrodynamic conditions in the White River prior to 2020 recharge are close to those of a typical dry representative year.

The calendar of the 2020 South-to-North Water Diversion Central Project's ecological water replenishment to the Baihe River is from May 9 to June 21, a total of 44 days, with a total water replenishment volume of 85.07 million m³, and the source of the replenishment is the Danjiangkou Reservoir, with the water quality stabilized at the Class II water standard. The recharge volume of ecological recharge in 2020 is defined as Q, and the recharge calendar time is defined as T. In order to explore the optimization scheme of ecological recharge, a consistent control method of total recharge is adopted, and the recharge volume is divided into 2Q, Q, and Q/2, corresponding to three recharge time durations of T/2, T, and 2T, respectively, with the initial water quality of the river being Class V. Simulate and analyze the impact of the above schemes on the river ecosystem to provide theoretical reference for the implementation of ecological water replenishment of the river, and the setting of the specific scheme is shown in Table 3–4.

Table 3

4 Scenario settings for non-recharge period
Water supply guarantee rate	Flow(m³/s)	Typical representative year	Initial water quality conditions	Makeup water flow rate(m³/s)	Water replenishment duration	Scenario Setting
P = 90%	12.04	2017	Class IV	2Q	1/2T	Scenario 1
P = 90%	12.04	2017	Class IV	Q	T	Scenario 2
P = 90%	12.04	2017	Class IV	1/2Q	2T	Scenario 3
(ii) Scenario parameterization
(1) Recharge inlet

The scenario for the recharge period is based on the process of ecological recharge of the Baihe River by the South-to-North Water Diversion Central Project in 2020, and the recharge inlet is the Baihe River receding sluice gate located in the main line of the South-to-North Water Diversion, and Danjiang River water transported by the main line of the South-to-North Water Diversion enters into the channel of the Baihe River directly through the receding sluice gate; therefore, the Baihe River receding sluice gate was selected as the inlet of the recharge for the recharge simulation scenarios during the recharge period.

(2) Total water recharge

In 2020, the total amount of ecological water replenishment to the Baihe River from the South-to-North Water Diversion Project reaches 85.07 million m³. After the replenishment, the hydrodynamic conditions of the Baihe River have been significantly improved, the ecosystem function of the study area is basically restored to the normal level, and the water quality indexes have reached the requirements of the water function zones, therefore, the total amount of ecological water replenishment in 2020 is selected as a basis condition for the scenario of the non-replenishment period, and the total amount of this replenishment is used to carry out a simulation of the scenario. The total amount of water recharge is used to control the simulation scenario.

(3) Make-up water flow

The flow rate of ecological recharge to the Baihe River from the South-to-North Water Diversion Central Project in 2020 will be 15–40 m³/s. The daily recharge duration and recharge flow rate data collected can be used to calculate the daily recharge flow rate. The process of ecological water recharge in scenarios1 ~ 3will be produced as a recharge flow time series file as shown in Fig. 3–6.

(4) Hydration duration

The duration of the 2020 South to North Water Diversion Middle Route Project to replenish water to the Baihe River ecosystem was from May 9th to June 21st, a total of 44 days, defined as T, as the standard duration for this simulation. The specific situation of water supplement duration can be reflected in Fig. 3–6.

(5) Parameter settings for water replenishment period

Before the water replenishment in 2020, the water quality of Baihe River was classified as Class IV water, so the initial water quality of the model was set as Class IV water standard according to the "Surface Water Environmental Quality Standard". The make-up water comes from the trunk line of the middle route of the South to North Water Transfer Project. The water source is Danjiangkou Reservoir, and the water quality is stable at the Class II water standard. Therefore, the quality of make-up water is based on the time series of the measured water quality at the Baihe River exit gate in 2020.

In order to better describe the evolution law of water quality in the simulated section of the Baihe River, it is necessary to set monitoring points in the model. This article sets up three monitoring points (M1, M2, M3) for real-time control of the water quality of the Baihe River. Among them, M1 is located near the Baihe Exit Sluice in the middle line of the South to North Water Diversion Project, M2 is located near the main urban area of Nanyang City, and M3 is located near the Shangfanying section of Nanyang. The specific location of control points is shown in Fig. 4 − 1.

4.1 Simulation analysis of 2 times the flow rate and 1/2 water replenishment time (Scenario 1)

Scenario 1 takes 2020 as the benchmark year for the model, and the initial water quality before ecological replenishment is the Class IV water standard. The replenishment flow process and water quality at the Baihe River outlet gate in the model are set, and a hydrodynamic water quality coupling model is constructed to simulate the ecological replenishment process in Scenario 1. The spatial distribution of simulated time-mean values for the five water quality indicators in the simulation results is shown in Fig. 4 − 2.

It can be seen from Fig. 4 − 2 that after the implementation of ecological water supplement, as the ecological water supplement is continuously replenished to the Baihe River, After improving the hydrodynamic conditions of the water body, the simulation showed that the DO content in the river section increased significantly, while the concentration of COD_Mn, COD, NH₃-N and TP decreased due to the enhanced diffusion of pollutants by the water flow. Under scenario 1, the overall concentration of DO is maintained between 3.6 and 9.2mg/L, the overall concentration of COD is maintained between 6 and 20mg/L, the overall concentration of COD_Mn is maintained between 4.48 and 5.04mg/L, the overall concentration of NH₃-N is maintained between 0.12 and 0.68mg/L, and the overall concentration of TP is maintained between 0.056 and 0.168mg/L.

From the performance of water quality indicators at monitoring points, it can be seen that the water quality at each monitoring point meets the standards for Class III water or above, with COD and TP indicators performing the best and reaching the Class II water standards. The M1 monitoring point is relatively close to the location of the make-up water outlet, and the water quality at this point is similar to that of the make-up water, with the best performance of water quality indicators. The M2 monitoring point is close to the location of the sewage discharge outlet, and the water quality at this point varies greatly. Each water quality indicator at the M2 monitoring point is greatly disturbed by pollutant emissions. The M3 monitoring point is located downstream of the urban section, and the changes in water quality indicators often lag behind. Under scenario one, the flow of ecological replenishment is much greater than the flow during the dry season. Ecological replenishment has a significant impact on the hydrodynamic conditions of rivers, with the hydrodynamic effect of rivers being the dominant factor, and the reduction of pollutants with the diffusion of water flow is significant.

To evaluate the improvement effect of ecological water replenishment on water quality, M2 monitoring point was selected as the water quality control point of Baihe River. When the model is stable, the concentrations of various water quality indicators at M2 monitoring points are shown in Table 4 − 1. Under scenario 1, the DO concentration increased to 5.84mg/L, meeting the Class III water standard, an increase of 94.67% compared to before water supplementation; The COD concentration decreased to 14.54mg/L, reaching the standard for Class II water, a reduction of 51.53% compared to before water replenishment; The concentration of COD_Mn decreased to 4.71mg/L, meeting the Class III water standard, a reduction of 52.90% compared to before water replenishment; The concentration of NH₃-N decreased to 0.54mg/L, meeting the Class III water standard, which was reduced by 64.00% compared to before water supplementation; The TP concentration decreased to 0.10mg/L, meeting the Class II water standard, which was 66.67% lower than before water supplementation. Overall, the water replenishment plan in Scenario 1 has a significant effect on improving the water quality of the Baihe River, with all water quality indicators meeting the water quality requirements of the water function zone. Among them, the DO indicator has the best improvement effect, while the TP indicator has the highest reduction rate. The changes in all water quality indicators follow the laws of natural evolution, achieving good simulation results.

Table 4

− 1 Scenario 4 Simulation results for each control point Unit: mg/L
Monitoring points	DO	COD	COD_Mn	NH₃-N	TP
M1	9.23	14.26	4.61	0.37	0.09
M2	5.84	14.54	4.71	0.54	0.10
M3	5.65	14.28	4.66	0.54	0.11

4.2 Simulation analysis of standard flow rate and standard water replenishment duration (Scenario 2)

In Scenario 2, the year 2020 is taken as the base year of the model, and the initial water quality before ecological water replenishment is Ⅳ water standard. The recharge flow process and recharge water quality at the retreat gate of Baihe River in the model are set, and the coupled hydrodynamic-water quality model is constructed to simulate the ecological water replenishment process in Scenario 2. The spatial distribution of the simulated time-averaged values of the five water quality indicators in the simulation results is shown in Fig. 4 − 3.

As can be seen in Fig. 4 − 3, after the implementation of ecological recharge, with the ecological recharge water continuously recharged to the channel of the White River to improve the hydrodynamic conditions of the water body, the simulation results show that under the conditions of Scenario 2, the DOcontent in the river section was maintained between 3.2 and 8.8 mg/L. At the same time, the concentration of pollutants such as COD and TP was reduced. At the same time, the COD_Mn, COD, NH₃-N and TP were also reduced, with COD concentrations ranging from 0 to 21 mg/L, COD_Mn concentrations from 5 to 5.56 mg/L, NH₃-N concentrations from 0.44 to 1 mg/L, TP concentrations from 0.44 to 1 mg/L, and TP concentrations from 1.5 to 1.8 mg/L. The simulation results showed that the DO content in the river section was maintained between 3.2 and 8.8 mg/L under Scenario 2 conditions. /L, and the concentration of TP was maintained between 0.072 and 0.184 mg/L overall. These results indicate that improving hydrodynamic conditions is a very important step in water environment management, and can significantly increase the dissolved oxygen content in the water body, thus effectively improving the local ecological environment. At the same time, reducing the discharge of all kinds of pollution sources is also an indispensable part of environmental protection. Therefore, we need to reduce the discharge of pollutants through various means and emphasize on the comprehensive analysis and assessment of local water flow characteristics and ecological trends.

From the performance of the water quality indicators at the monitoring points, it can be seen that the water quality at each monitoring point meets the standard of Class III water or above. The M1 monitoring point is relatively close to the location of the make-up water outlet, and the water quality at this point is similar to that of the make-up water, with the best performance of water quality indicators. The M2 monitoring point is close to the location of the sewage discharge outlet, and the water quality at this point varies greatly. Each water quality indicator at the M2 monitoring point is greatly disturbed by pollutant emissions. The M3 monitoring point is located downstream of the urban section, and the changes in water quality indicators often lag behind. Under scenario 2, the flow of ecological replenishment decreased by half compared to scenario 1, and the impact of ecological replenishment on river water dynamics was weakened, while the reduction of pollutants with water diffusion was reduced.

To evaluate the improvement effect of ecological water replenishment on water quality, M2 monitoring point was selected as the water quality control point of Baihe River. When the model is stable, the concentrations of various water quality indicators at M2 monitoring points are shown in Table 4 − 2. Under scenario 2, the DO concentration increased to 5.61mg/L, meeting the Class III water standard, an increase of 87% compared to before water supplementation; The COD concentration decreased to 16.00mg/L, meeting the Class III water standard, a reduction of 46.67% compared to before water replenishment; The concentration of COD_Mn decreased to 5.37mg/L, meeting the Class III water standard, a reduction of 46.30% compared to before water replenishment; The concentration of NH₃-N decreased to 0.87mg/L, meeting the Class III water standard, a 42.00% reduction compared to before water supplementation; The TP concentration decreased to 0.15mg/L, meeting the Class III water standard, a reduction of 50.00% compared to before water supplementation. Overall, the water replenishment plan in Scenario 2 has a significant effect on improving the water quality of the Baihe River, with all water quality indicators meeting the water quality requirements of the water function zone. Among them, the DO indicator has the best improvement effect, and the COD indicator has the highest reduction rate. The changes in all water quality indicators comply with the laws of natural evolution, achieving good simulation results.

Table 5

5 Scenario 5 Simulation results for each control point Unit: mg/L
Monitoring points	DO	COD	COD_Mn	NH₃-N	TP
M1	9.12	14.62	5.10	0.46	0.11
M2	5.61	16.00	5.37	0.87	0.15
M3	5.58	15.74	5.15	0.84	0.13

4.3 Simulation analysis of 1/2 flow rate and 2 times water replenishment time (Scenario 3)

In Scenario 3, the year 2020 is taken as the base year of the model, and the initial water quality before ecological recharge is class IV. The recharge flow process and recharge water quality at the retreating gate of the White River in the model are set, and the coupled hydrodynamic-water quality model is constructed to simulate the ecological recharge process in Scenario 3. The spatial distribution of the simulated time-averaged values of the five water quality indicators in the simulation results is shown in Fig. <link rid="fig9">4</link>–4.

As can be seen from Fig. <link rid="fig9">4</link>–4, after the implementation of ecological recharge, with the continuous recharge of ecological recharge to the channel of the White River, after improving the hydrodynamic conditions of the water body, the simulation shows that the DO content in the river section is significantly enhanced. At the same time, due to the enhancement of water diffusion, pollutants were better dispersed and diluted in the water body, which led to a reduction in the concentrations of CODMn, COD, NH3-N and TP. These results indicate that by improving the hydrodynamic conditions and taking corresponding measures to reduce the discharge of pollution sources, the quality of the local water environment can be effectively improved and the impacts of environmental pollution on the ecosystem and human health can be reduced. Therefore, the flow characteristics of water bodies should be emphasized in the future development and implementation of relevant policies, as well as the integrated management of various pollution sources. Under the conditions of Scenario 3, the concentration of DO was maintained between 3.6 and 9.2 mg/L, the concentration of COD was maintained between 6 and 20 mg/L, the concentration of CODMn was maintained between 4.48 and 5.04 mg/L, the concentration of NH3-N was maintained between 0.12 and 0.68 mg/L, and the concentration of TP was maintained between 0.056 and 0.168mg/L.

From the performance of the water quality indicators at the monitoring points, it can be seen that the water quality at each monitoring point meets the standard of Class III water or above. The M1 monitoring point is relatively close to the location of the make-up water outlet, and the water quality at this point is similar to that of the make-up water, with the best performance of water quality indicators. The M2 monitoring point is close to the location of the sewage discharge outlet, and the water quality at this point varies greatly. Each water quality indicator at the M2 monitoring point is greatly disturbed by pollutant emissions. The M3 monitoring point is located downstream of the urban section, and the changes in water quality indicators often lag behind. Under scenario three, the flow of ecological replenishment is reduced by half compared to scenario two, and the impact of ecological replenishment on river water dynamics is further weakened. The effect of pollutant reduction with water diffusion is further reduced.

To evaluate the improvement effect of ecological water replenishment on water quality, M2 monitoring point was selected as the water quality control point of Baihe River. When the model is stable, the concentrations of various water quality indicators at M2 monitoring points are shown in Table 4 − 3. Under scenario three conditions, the DO concentration increased to 5.52mg/L, meeting the Class III water standard, an increase of 84.00% compared to before water supplementation; The COD concentration decreased to 18.00mg/L, meeting the Class III water standard, a 40.00% reduction compared to before water replenishment; The concentration of COD_Mn decreased to 5.98mg/L, meeting the Class III water standard, a reduction of 40.20% compared to before water replenishment; The concentration of NH3-N decreased to 0.96mg/L, meeting the Class III water standard, a reduction of 36.00% compared to before water supplementation; The TP concentration decreased to 0.19mg/L, meeting the Class III water standard, a reduction of 36.33% compared to before water supplementation. Overall, the water replenishment plan in scenario three has a significant effect on improving the water quality of the Baihe River, with all water quality indicators meeting the water quality requirements of the water function zone. Among them, the DO indicator has the best improvement effect, while the COD_Mn indicator has the highest reduction rate. The changes in all water quality indicators comply with the laws of natural evolution, and good simulation results have been obtained.

Table 4

− 3 Scenario 6 simulation results for each control point Unit: mg/L
Monitoring points	DO	COD	COD_Mn	NH₃-N	TP
M1	9.00	14.65	5.50	0.55	0.12
M2	5.52	18.00	5.98	0.96	0.19
M3	5.41	16.63	6.20	0.92	0.18

4.4 Future Development

(1) In this study, model parameters such as eddy viscosity coefficient and Coriolis force were set as constants during parameter setting. Future research can consider setting these parameters as variables that can change with time and space, further improving the accuracy of the model.

(2) This study does not consider the confluence of many small tributaries of the Baihe River, and the hydrological data of some years in the study area is missing. More comprehensive data can be collected later to build a large-scale basin Hydrological model, and the synergy between basins can be comprehensively considered.

(3) Due to space limitations, this article only considers three scenarios for scenario settings. We will add more scenario settings in future research to seek better ecological water replenishment solutions.

1) From the simulation results, it can be seen that under a certain total amount of water replenishment, the scenario of using high flow water replenishment has better hydrodynamic conditions, has a significant effect on counteracting the erosion of silt at the bottom of the river, and has a better effect on improving the water quality of the receiving river. The water quality of rivers has been upgraded from the Class IV water standard to the Class III water standard. Among them, the COD and TP indicators have been upgraded to the Class II water standard, with the highest improvement rate of 94.67% for DO and the best reduction effect of 66.67% for TP indicators.

2) From the changes in the concentration of water quality indicators at the monitoring point, it can be seen that the M1 monitoring point is closer to the location of the make-up water outlet, and the water quality at this point is similar to that of the make-up water. The performance of the water quality indicators is the best. The M2 monitoring point is close to the location of the sewage discharge outlet, and the water quality at this point varies greatly. Each water quality indicator at the M2 monitoring point is greatly disturbed by pollutant emissions. The M3 monitoring point is located downstream of the urban section, and the changes in water quality indicators are usually lagging behind.

3) Overall, under non flood season conditions, the Baihe River often has better water quality conditions due to its smaller confluence, while during flood season, it is often affected by the accumulation of multiple pollutants and has poorer water quality conditions. The water quality conditions during the river replenishment period are better than those during the non replenishment period, and the water quality indicators show a spatial pattern of upstream being better than downstream. Among the three water replenishment scenarios, the scenario with 2 times the flow rate and 1/2 water replenishment time (Scenario 1) has the best water replenishment effect.

Funding

No funding was received to assist with the preparation of this manuscript.

Competing Interests

The authors declare no conflict of interest.

Ethical Approval

This paper does not contain any studies with human participants or animals performed by any of the authors.

Consent to Participate

Consent.

Consent to Publish

Consent.

Authors Contributions

Zhang XQ: Methodology, Investigation, Writing-Original draft preparation.

Chen P: Conceptualization, Writing-review & editing.

Dai SN: Methodology, Formal analysis.

Han YH: Conceptualizaiton, Writing-original draft preparation.

Chang X: Conceptualizaiton, Writing-original draft preparation.

Availability of data and materials

Datasets and other materials are available with the authors, and may be accessible at any time upon request.

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Simulation study on the impact of ecological water recharge on the riverine environment based on Mike21

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Abstract

Figures

1 Introduction

2 Overview of the study area

2.1 Geographical location

2.2 Recharge situation

3 Data and methodology

3.1 Model Establishment

3.2 Parameter selection and ratemaking

3.3 Water quality model validation

3.4 Scenario setting

4 Results and Discussion

4.1 Simulation analysis of 2 times the flow rate and 1/2 water replenishment time (Scenario 1)

4.2 Simulation analysis of standard flow rate and standard water replenishment duration (Scenario 2)

4.3 Simulation analysis of 1/2 flow rate and 2 times water replenishment time (Scenario 3)

4.4 Future Development

5 Conclusion

Declarations

References

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