With global climate change and extreme weather events, water scarcity and environmental degradation are becoming increasingly prominent. Ecological replenishment is one important measure to enhance water quality in reservoirs. To address the severe pollution and ecological degradation problems of the Luhun Reservoir, this paper uses MIKE21 software to construct a coupled hydrodynamic-water quality model. Six recharge scenarios were designed to compare and explore the enhancement of reservoir water quality for these scenarios. The water quality status of these options was evaluated using the single-factor index method and the combined pollution index method. The results show that due to the ecological replenishment, the pollutant concentration leads the phenomenon of gradient diffusion to the center of the reservoir, and the average improvement rate of water quality is up to 40.22%. Schemes 2 and 5 have the lowest integrated pollution index. The most significant improvement in water quality was achieved at the same total recharge conditions, using 0.5 times the flow rate and two times the recharge time of the actual recharge project. The study results provide theoretical and technical support for the future management of the reservoir water environment.

  • Verification of hydrodynamics and water quality in Luhun Reservoir using MIKE21 software.

  • The single-factor index method and the integrated pollution index method can accurately reflect the cleanliness of the watershed.

  • Using the actual project 0.5 times the flow rate and two times the recharge time can improve the water quality of Luhun Reservoir more effectively.

As an essential national water infrastructure, reservoirs have functions such as flood control, water supply, power generation, irrigation, navigation and ecological protection (Branche 2017). It occupies an essential position in the rational development and utilization of water resources and sustainable social development. With the rapid development of the social economy, a large amount of domestic sewage, farmland drainage and industrial wastewater flows into the reservoir. It has increased the pollution load of organic matter and nutrients such as nitrogen and phosphorus in reservoirs, resulting in severe pollution of most reservoir waters (Wang et al. 2021). Therefore, scientific and effective integrated management of the water environment is a significant project to guarantee the regular operation of the reservoir and restore a good water environment (Zhuang 2016). Based on the increasingly severe problem of water pollution, scholars at home and abroad have carried out theoretical research and engineering practice and proposed various methods to achieve the goal of water environment improvement and protection. The main methods include physical, chemical and bioecological (Qu & Fan 2010). Chemical methods control water pollution and improve the environment by separating or recovering pollutants from water bodies through chemical reactions. However, the investment is significant, the duration of the effect is short and it tends to cause secondary pollution of water bodies (Sharma & Bhattacharya 2017). The bioecological approach refers to the uptake, conversion or decomposition of pollutants in water by microorganisms, thus improving or even eliminating water pollution and restoring water ecological functions (Cao et al. 2020). However, the bioecological approach may lead to ecological risks. Physical methods are the most widely used methods in reservoir water environment management and improvement (Al-Jawad et al. 2019). It mainly includes measures such as bottom dredging and ecological recharge (Huang et al. 2022). Substrate dredging reduces or even removes endogenous pollution from reservoirs by removing nutrient-rich, toxic chemical and toxin-rich bacterial surface sediments from the underside of the water column and preventing the transfer of pollutants from the substrate into the water column (Peterson 1982). However, bottom dredging may harm the reservoir's ecosystem, so there is a degree of uncertainty about the effectiveness of water quality improvements (Yasarer & Sturm 2016). In certain reservoirs, inadequate water exchange capacity caused by factors such as topography and inlet conditions can hinder the overall or local water flow, resulting in poor water quality. In such cases, solely relying on bottom dredging may not be sufficient to achieve significant improvements (Gao et al. 2019). Thus, enhancing the hydrodynamic conditions of the reservoir area through ecological replenishment is crucial for improving the water environment. Ecological recharge refers to the introduction of water bodies with better water quality around the reservoir area into the reservoir through certain engineering measures to improve the reservoir hydrodynamic and water quality conditions (Zhang et al. 2022). Herath et al. (2022) assessed the heavy metal pollution in the Three Gorges Reservoir area after ecological replenishment using the pollution index method and found a reduction in heavy metal pollution. Yan et al. (2018) studied the environmental water demand of the Pingshan River Basin in Shenzhen, China, using water resource allocation and simulation models. The results indicated a significant increase in the basin's ecological water demand, reaching satisfactory levels after replenishment. Mao et al. (2020) employed hydrological and hydrodynamic methods to investigate the optimal operation of reservoirs under different recharge scenarios, providing a reliable basis for reservoir operation and scheduling. Li et al. (2020a, 2020b) conducted a quantitative study of the effect of hydrodynamic changes on Lake Qi's water quality using MIKE21 water environment software. They found that ecological replenishment led to a significant improvement in lake hydrodynamics, a decrease in the concentration of internal pollutants and a gradual improvement in water quality. These studies have demonstrated that ecological replenishment through rational water resource scheduling can enhance hydrodynamic conditions and improve water quality (Lu et al. 2022). Ecological replenishment has emerged as a primary approach to enhance reservoir water environments. However, due to the intricate nature of reservoir water characteristics, it is crucial to simulate the impact of replenishment on the water environment. Although domestic and foreign scholars have made notable progress in studying ecological water replenishment, certain key aspects of simulation remain understudied. Existing research on ecological water replenishment analysis typically relies solely on actual projects, focusing narrowly on the location of replenishment and the allocation of water quality. Consequently, it overlooks crucial factors such as the initial water quality of the receiving area and the establishment of water replenishment hydrodynamic conditions. This study introduces a novel approach by incorporating a hierarchical water quality framework within the receiving area, while ensuring the overall volume of replenishment aligns with project implementation. By adjusting the flow rate and timing of replenishment, this research offers fresh perspectives and strategies for future ecological water replenishment initiatives. This study employs MIKE21 software to construct a coupled hydrodynamic-water quality model for the numerical simulation of the Luhun Reservoir. The existing water regulation measures are fully utilized to design six recharge schemes based on the current project. The improvement of water quality in reservoirs under different scenarios is explored. The advantages and disadvantages of various techniques are compared using the single-factor and integrated pollution index methods. The research findings provide scientific support for decision-making regarding reservoir water environment management and efficient utilization of water resources.

Study area

Luhun Reservoir is situated in the middle reaches of the Yi River, a tributary of the Yellow River. The basin is geographically positioned between 111°23′–112°51′E longitude and 33°51′–34°3′N latitude. The total length of the river is 264.8 km, with a watershed area of 6,029 km2. The control basin area of the Luhun Reservoir is 3,492 km2, accounting for 57.9% of the basin area of the Yi River. The average multi-year rainfall of the Luhun Reservoir is 787.2 mm, and the inter-annual variation is large. The variation of flow during the year depends entirely on the variation of rainfall in all seasons. Rain and snow are scarce in winter and spring, and the river flow mainly relies on groundwater recharge, with low flow (Zhang et al. 2021). June to October for the occurrence of heavy rainfall season, including July and August heavy rainfall concentration, high intensity, flood flow is the largest. The maximum flow observed at the reservoir inlet control station (Dongwan station) on 9 August 1975 was 4,200 m3/s; the minimum flow observed on 15 July 1974 was 1.42 m3/s. Figure 1 displays the precise geographical coordinates of Luhun Reservoir's location.
Figure 1

Location of Luhun Reservoir.

Figure 1

Location of Luhun Reservoir.

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MIKE21 model principles

MIKE21 software has stable, fast and reliable operation characteristics. The computational approach used in this study is based on the cell-centered finite volume method, which is employed to solve the continuity and momentum equations for water flow discretely. The MIKE21 model is a two-dimensional free-surface flow model that incorporates the hydrodynamic module (HD) for simulating water level and flow changes caused by different forces. This module serves as the foundation for hydrodynamic calculations in environmental simulations. In a study conducted by He et al. (2021), a planar 2D free-surface flow model was used to simulate water quality data in the downstream area of Liangxi River, adjacent to Taihu Lake. The performance of the model was compared with spatial interpolation using a test set, and it demonstrated favorable results. Similarly, Tang et al. (2018) applied the MIKE21 model, encompassing the 2D hydrodynamic and water quality modules, to the Baiyangdian, a large shallow lake. They simulated 18 different water transfer scenarios and proposed ecological restoration measures for Baiyangdian based on the lake's hydrodynamics and flow direction under these scenarios. Jiang et al. (2022) constructed a two-dimensional hydrodynamic and water quality mathematical model of Hongze Lake using the MIKE21 software. The model was used to simulate the changes in nitrogen and phosphorus concentrations in the lake following water transfer, allowing for the identification and discussion of the underlying causes of these changes. The MIKE21 water environment model encompasses both the HD module and the water quality Ecolab simulation processes, making it suitable for accurately simulating and analyzing ecological replenishment processes. Hence, in this study, the MIKE21 model was utilized to establish a two-dimensional hydrodynamic-water quality model for the Luhun Reservoir in Luoyang.

Hydrodynamic module

The hydrodynamic module is the basis for the water quality module simulation. It mainly simulates the flow patterns of water bodies under the combined effects of various external forces, such as wind fields, ice caps, tides, rainfall and other factors. The module is based on the basic data, boundary conditions and solution format, which can truly and effectively reflect the changes of water flow field in the study area by the eddy viscosity coefficient and external forces (Liang et al. 2015). The hydrodynamic module of MIKE21 mainly relies on gridding the terrain, inputting the bottom bed roughness, boundary conditions, rainfall, evaporation, wind field, ice cover, eddy viscosity coefficient, Coriolis force and other influencing factors to simulate various flow field elements such as water level and flow rate. This approach has extensive applications in simulating shallow water flow with a free surface in two dimensions.

The equations for the hydrodynamic module are shown below:
(1)
(2)
(3)
where t is the study duration; x, y are the two axes in the Cartesian coordinate system;, h is the total head height; is the water level elevation; d is the river bottom elevation; u, v are the component of the velocity in the x and y directions; , are the average velocity of flow along the water depth; f is the Coriolis force coefficient, the calculation formula is ; is the angular velocity of the Earth's rotation, is the geographical latitude; g is the acceleration of gravity; is the density of water; , , are the radiation stress components; S is a point source item; , is the source term water flow rate; , , , are different shear stress in many directions; is the horizontal viscous stress term, which includes viscous forces, turbulent stress and horizontal convection. The calculation formula is:
(4)
(5)
(6)

Water quality module

ECO Lab is a water ecology tool developed based on the water quality module to simulate changes in water quality, water eutrophication, heavy metal transport and other patterns. The main applications are in river, wetland, lake, reservoir, estuary, riparian and marine watersheds (Li et al. 2020a, 2020b):
(7)
where c is the concentration of the state variable; u, v, w are the flow velocity component of the convection term; , , are the current flow component of the convection term; is the source–sink item; is a chemical reaction process, it generally refers to the factors of change in the concentration of substances in the water column, with linear or non-linear coupling between the state variables through the process term , and the expression is:
(8)
where n represents the sum of the process quantities occurring between the variables.
ECO Lab water quality module is a combination of multiple components, of which the core component is ECO Lab COM. The main role is to integrate the water quality module with other modules and complete the simulation of water quality data calculation process. Water quality in the simulation process is required to use the compiler in the ECO Lab COM component to translate the required series of commands step by step, in collaboration with other modules to complete the whole process of water quality simulation (Fu et al. 2019). After the simulation starts, at the first-time step, ECO Lab module will first analyze various parameters and variables of the hydrodynamic model and integrate the final simulation results, and then cycle through the whole simulation process. The overall simulation process flowchart will be drawn as shown in Figure 2.
Figure 2

The data flow in the research of hydrodynamic and water quality mode.

Figure 2

The data flow in the research of hydrodynamic and water quality mode.

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Water quality evaluation methods

This paper used the single-factor and integrated pollution indexes to evaluate the water quality (Sargaonkar & Deshpande 2003). The single-factor index method is the ratio of the observed value of each water quality index and the highest acceptable standard of environmental water quality, which calculates the single-factor pollution index. The single-factor pollution index formula is:
(9)
where is the category i single-factor pollution index; is the observed concentration (mg/L) for category i indicators and is the evaluation standard value (mg/L) of the index of category i.
The formula for calculating special water quality indicators is shown below:
(10)
(11)
(12)
where is the single-factor pollution index of dissolved oxygen (DO); is the observed concentration value of DO (mg/L); is the evaluation standard value of DO (mg/L); is the value of DO concentration (mg/L) that reaches saturation at T water temperature and T is the observed value of water temperature (°C). The criteria for each water quality indicator are shown in Table 1.
Table 1

Water quality assessment criteria (unit: mg/L)

No.Evaluation factorsGrading criteria
Category ICategory IICategory IIICategory IVCategory V
Dissolved oxygen (DO) ≥7.5 
Ammonia nitrogen (NH3-N) ≤0.15 0.5 1.5 
Total nitrogen (TN) ≤0.2 0.5 1.5 
Total phosphorus (TP) ≤0.01 0.025 0.05 0.1 0.2 
No.Evaluation factorsGrading criteria
Category ICategory IICategory IIICategory IVCategory V
Dissolved oxygen (DO) ≥7.5 
Ammonia nitrogen (NH3-N) ≤0.15 0.5 1.5 
Total nitrogen (TN) ≤0.2 0.5 1.5 
Total phosphorus (TP) ≤0.01 0.025 0.05 0.1 0.2 
The integrated pollution index is obtained on the basis of single-factor evaluation method, which can qualitatively reflect the comprehensive pollution level of reservoir water bodies and classify the level according to relevant standards. It is an essential method for evaluating the quality of the water environment, and the calculation formula is as follows:
(13)

The integrated pollution index is shown in Table 2.

Table 2

Integrated pollution index method evaluation grading standard

Integrated pollution index PWater quality conditionGrading reference basis
≤0.20 Clean Most water quality factors were not detected, and even if they were detected, they were within the standard values 
0.21–0.40 Cleaner Several water quality factors were detected but within the standard values, only individual water quality factors were close to the standard values 
0.41–0.70 Light pollution Individual water quality factors exceed the standard values, but the exceedance multiplier is small 
0.71–1.00 Medium pollution Few water quality factors were detected and exceeded standard values 
1.01–2.00 Heavy pollution Most water quality factors were detected and exceeded standard values 
>2.00 Serious pollution Most water quality factors were detected and exceeded several times or tens of times 
Integrated pollution index PWater quality conditionGrading reference basis
≤0.20 Clean Most water quality factors were not detected, and even if they were detected, they were within the standard values 
0.21–0.40 Cleaner Several water quality factors were detected but within the standard values, only individual water quality factors were close to the standard values 
0.41–0.70 Light pollution Individual water quality factors exceed the standard values, but the exceedance multiplier is small 
0.71–1.00 Medium pollution Few water quality factors were detected and exceeded standard values 
1.01–2.00 Heavy pollution Most water quality factors were detected and exceeded standard values 
>2.00 Serious pollution Most water quality factors were detected and exceeded several times or tens of times 

Model construction

Grid division

The study area has meshed with an unstructured Delaunay triangular mesh. According to the scale of the computational domain, the grid scale is set to 80 m, and 2,612 nodes and 4,828 grid cells are generated. The overall division of the mesh is smooth and natural, and the sparse and dense transition is uniform, which can ensure the stability of the two-dimensional model and improve the simulation accuracy. The inlet in the southwest and the outfall in the northeast are defined as open boundaries, and the rest of the shoreline is the land boundary. The topographic map is then generated by importing the topographic discrete data and interpolating the generated grid. The grid division and topographic map of the Luhun Reservoir are shown in Figure 3.
Figure 3

Grid division and topography of Luhun Reservoir.

Figure 3

Grid division and topography of Luhun Reservoir.

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Parameter setting

Hydrodynamic model parameter setting
  • (1)

    Solution technique

The hydrodynamic model can choose from two methods – lower-order and higher-order. The low-order accuracy model takes less time to calculate, but the accuracy of the results is lower. In contrast, the high-order accuracy calculation method takes longer but has higher accuracy. After considering the computational time cost and the accuracy of the results, this paper chose the higher-order way for this study. The CFL number (Courant–Friedrich–Levy number) is generally set to less than 1. In this study, the CFL number of 0.8 is used.

  • (2)

    Time step

The time of this simulation is from 1 January 2021, 00:00:00 to 1 January 2022, 00:00:00, setting the time step to 1,800 s and the number of time steps to 17,520.

  • (3)

    Flood and dry

The simulated area is in the alternating wet and dry boundary area, so to avoid the instability of the model in operation, it is necessary to set the dry water depth, the inundation depth and the water depth. According to the principle of the definition of wet and dry water depth, the model pre-set values were used in this study after several times of debugging.

  • (4)

    Eddy viscosity

The eddy viscosity coefficient is divided into horizontal and vertical vortex viscosity coefficients. This study is a two-dimensional hydrodynamic simulation, so only the horizontal eddy viscosity coefficient is considered. There are three methods for setting the model eddy viscosity coefficients: vortex-free, constant vortex formula and Smagorinshy formula. In this study, the hydrodynamic model used the Smagorinsky formula to calculate the horizontal eddy viscosity coefficient, and its coefficient was chosen from a predetermined value of 0.28 in the model.

  • (5)

    Bed resistance

The bottom bed roughness is a comprehensive coefficient that responds to the water flow resistance and is considered according to the actual condition of the reservoir bottom bed. There are three model roughness setting methods: bottomless bed friction, Chezy coefficient and Manning coefficient. This model uses the Manning coefficient to calculate the streambed roughness, and the Manning coefficient field of this study is shown in Figure 4.
  • (6)

    Wind forcing

Figure 4

Manning’ M distribution map of Luhun Reservoir.

Figure 4

Manning’ M distribution map of Luhun Reservoir.

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The wind is a crucial energy source that propels water movement in a reservoir. It can enhance turbulence in the water column beneath the reservoir surface, and for this study, the observed time series value of the wind field was used.

  • (7)

    Precipitation-evaporation

This study area has the effects of rainfall and evaporation, so their effects must be considered when building the 2D hydrodynamic model, with the same observed time series values taken for precipitation and evaporation.

  • (8)

    Initial conditions

The initial conditions of the model in the simulation process are a vital part which will directly affect whether the hydrodynamic model can operate properly and the accuracy of the simulation results. The initial conditions are the reservoir area's initial water level and the flow field's initial flow velocity, etc. This study's initial water level is 320 m, and the initial flow velocity is 0 m/s. In the hydrodynamic model, there are six boundary conditions: land boundary with zero vertical flow velocity, the land boundary with zero flow velocity and velocity boundary. In this study, the inlet boundary condition of the Luhun Reservoir is set as the flow boundary, and the outlet boundary condition is set as the water level boundary.

Water quality model parameter setting

Water quality model parameter setting is a very important part of the water quality simulation. This study parameter setting is after a long period of trial and error, relevant experience and review of relevant information, and repeated simulation, until the parameters are adjusted to a more reasonable range of values. The parameter settings of ECO Lab water quality module in this study are shown in Table 3.

Table 3

Water quality model parameter setting

Status variablesRelevant parametersValueUnit
BOD First-order degradation rate (dissolved state) 0.46 /day 
 Degradation temperature coefficient (dissolved state) 1.12  
 Oxygen concentration half-saturation number mg/L 
DO Maximum oxygen production rate at noon (/m2/day 
 Plant respiration rate (/m20.125 /day 
 Respiration temperature coefficient 1.08  
 Respiratory half-saturation number mg/L 
 Substrate oxygen demand (/m20.5 /day 
 Substrate oxygen demand temperature coefficient 1.07  
 Substrate oxygen demand half-saturation number mg/L 
NO2 First-order decay rate of nitrification 0.05 /day 
 Decay rate temperature coefficient (NH4-NO21.06  
 Decay rate temperature coefficient (NO2-NO31.06  
 Nitrification oxygen demand (NH4-NO23.35 g O2/g NH3-N 
 Nitrification oxygen demand (NO2-NO31.21 g O2/g NO3-N 
 Nitrification oxygen half-saturation number mg/L 
NH4 BOD decay release ammonia ratio (dissolved state) 0.3 g NH3-N/g BOD 
 Plant uptake of ammonia nitrogen 0.066 g N/g DO 
 Bacterial uptake of ammonia nitrogen 0.103 g N/g DO 
 Ammonia intake half-satiety number 0.05 mg/L 
NO3 First-order denitrification rate 0.1 /day 
 Temperature coefficient of denitrification rate 1.16  
PO4 Dissolved BOD phosphorus content 0.06 g P/g BOD 
 Phosphate uptake by plants 0.008 g P/g DO 
 Bacterial uptake of phosphate 0.015 g P/g DO 
 Phosphorus intake half-satiation number 0.005 mg/L 
Status variablesRelevant parametersValueUnit
BOD First-order degradation rate (dissolved state) 0.46 /day 
 Degradation temperature coefficient (dissolved state) 1.12  
 Oxygen concentration half-saturation number mg/L 
DO Maximum oxygen production rate at noon (/m2/day 
 Plant respiration rate (/m20.125 /day 
 Respiration temperature coefficient 1.08  
 Respiratory half-saturation number mg/L 
 Substrate oxygen demand (/m20.5 /day 
 Substrate oxygen demand temperature coefficient 1.07  
 Substrate oxygen demand half-saturation number mg/L 
NO2 First-order decay rate of nitrification 0.05 /day 
 Decay rate temperature coefficient (NH4-NO21.06  
 Decay rate temperature coefficient (NO2-NO31.06  
 Nitrification oxygen demand (NH4-NO23.35 g O2/g NH3-N 
 Nitrification oxygen demand (NO2-NO31.21 g O2/g NO3-N 
 Nitrification oxygen half-saturation number mg/L 
NH4 BOD decay release ammonia ratio (dissolved state) 0.3 g NH3-N/g BOD 
 Plant uptake of ammonia nitrogen 0.066 g N/g DO 
 Bacterial uptake of ammonia nitrogen 0.103 g N/g DO 
 Ammonia intake half-satiety number 0.05 mg/L 
NO3 First-order denitrification rate 0.1 /day 
 Temperature coefficient of denitrification rate 1.16  
PO4 Dissolved BOD phosphorus content 0.06 g P/g BOD 
 Phosphate uptake by plants 0.008 g P/g DO 
 Bacterial uptake of phosphate 0.015 g P/g DO 
 Phosphorus intake half-satiation number 0.005 mg/L 

After reviewing the relevant data and information, the initial concentration values of the state variables of Luhun Reservoir were determined as shown in Table 4.

Table 4

Water quality parameters of Luhun Reservoir

Status variablesBODDONH4NO2NO3PO4
Concentration (mg/L) 0.76 12.80 0.062 0.026 4.32 0.28 
Status variablesBODDONH4NO2NO3PO4
Concentration (mg/L) 0.76 12.80 0.062 0.026 4.32 0.28 

Model verification

Hydrodynamic model verification

This paper selected the observation data from Luhun hydrological station to validate the hydrodynamic model. The comparison of simulated and observed water levels is shown in Figure 5. The simulated and observed water level curves are basically similar in trend. The significant difference between the simulated water level and the observed data in the first period is due to the considerable dependence on the initial water level condition in the first simulation period. The simulation results are gradually close to the observed data as the simulation proceeds. The water level drops during the non-flood period and rises during the flood period, consistent with the actual situation. Simulation and observation of the water level relative error graph in Figure 6, where the maximum relative error occurs on September 27, the ultimate value of 0.085%, statement of the average annual water level of 319.81 m, simulation of the intermediate annual water level of 319.79 m, the relative error of 0.007%, the overall error is within the acceptable error range, that can be coupled with the water quality model.
Figure 5

Comparison of simulated and observed water levels at Luhun hydrological station.

Figure 5

Comparison of simulated and observed water levels at Luhun hydrological station.

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Figure 6

Relative error between simulated and observed water levels at Luhun hydrological station.

Figure 6

Relative error between simulated and observed water levels at Luhun hydrological station.

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Water quality model verification

For ECO Lab water quality model validation, DO, NH3-N and derived variables TN and TP were selected as validation indicators to assess the water quality model's accuracy and verify the model's feasibility. The validation results are shown in Figure 7.
Figure 7

Comparison between simulated and observed data of water quality status variables at Luhun hydrological station.

Figure 7

Comparison between simulated and observed data of water quality status variables at Luhun hydrological station.

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NSE and MAPE were used to evaluate the merits of the water quality model. The water quality simulation results were evaluated as shown in Table 5. It can be seen that the NSE of each state variable is greater than 0.8 and the MAPE is less than 10%. This indicates that the proposed ECO Lab water quality model can better reflect the water quality variation pattern of Luhun Reservoir. So, it can be used for various scenario analyses.

Table 5

Evaluation of water quality simulation results

Evaluation indicatorsDONH3-NTNTP
NSE 0.847 0.809 0.913 0.853 
MAPE 6.275% 9.865% 4.095% 7.693% 
Evaluation indicatorsDONH3-NTNTP
NSE 0.847 0.809 0.913 0.853 
MAPE 6.275% 9.865% 4.095% 7.693% 

Case setting

In this paper, based on the careful consideration of the ecological water demand requirements of Luhun Reservoir and the current situation of water replenishment, and on the regulations of Luhun Reservoir drinking water source protection in Luoyang City, the feasibility of realistic water replenishment conditions is considered. The Luhun Reservoir water replenishment simulation study is carried out by combining the upstream reservoirs and the main water replenishment channels of the South-North Water Diversion Central Project. Concerning 16:00 on 16 August 2022, the Luhun reservoir was recharged with a flow rate of 30 m3/s and a duration of 5 days; six recharge scenarios were set up here, as shown in Table 6. The recharge water quality is set to category II, whereas the initial water quality level of the first three programs is category III. 2162. In comparison, the initial water quality level of the last three programs is category IV.

Table 6

Water recharge scheme settings

SchemeInitial water quality levelHydration flow (m3/s)Hydration time (days)
Category III 10 15 
15 10 
30 
Category IV 10 15 
15 10 
30 
SchemeInitial water quality levelHydration flow (m3/s)Hydration time (days)
Category III 10 15 
15 10 
30 
Category IV 10 15 
15 10 
30 

In order to accurately analyze the change pattern of each pollutant index in the study area under the set working conditions, three points are selected to monitor the change of pollutants in this model, namely A1, A2 and A3. The locations of the monitoring points are shown in Figure 8.
Figure 8

Location of monitoring points in the model.

Figure 8

Location of monitoring points in the model.

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Water quality simulation results

Scheme 1–3 results

The water quality simulation results of schemes 1–3 are shown in Figure 9. By comparing the water quality of these three schemes, it can be found that the water quality of the inlet is optimal in all three scenarios. This is because the water quality effect of the channel in the case of water replenishment in the initial state is to meet the category II water. In contrast, the initial water quality of the reservoir is category III water. The condition of the water body at the outlet is also relatively good due to the good environmental conditions at the outlet, where the water velocity is blocked, and the water body has a strong purification capacity. From the simulation, the results show that the water quality in the narrow area under these three schemes is better than that in the broad area. This is because the water replenishment program increases the water flow of the water body flushing effect, enhances the water quality flow capacity between the water bodies and promotes the dilution concentration of other water quality parameters such as nitrogen and phosphorus in the water body, to effectively improve the water quality capacity of the reservoir area. The hydrodynamic effect is dominant at the center of the reservoir, and there is an apparent reflux phenomenon in the waters on both sides of the pool. The pollutant concentration shows the phenomenon of gradient diffusion to the center of the reservoir. The DO concentrations were 7.25, 7.7 and 7.5 mg/L, NH3-N concentrations were 0.55, 0.52 and 0.61 mg/L, TN concentrations were 0.84, 0.76 and 0.78 mg/L and TP concentrations were 0.036, 0.040 and 0.038 mg/L under the three scenarios at the central monitoring site A2 of the reservoir.
Figure 9

Water quality simulation results of schemes 1–3.

Figure 9

Water quality simulation results of schemes 1–3.

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As the water quality at the inlet and outlet varies greatly, it is not easy to reflect the water quality condition of the whole reservoir area, so the water quality at the center of the reservoir is selected as the object of analysis. When the initial water quality of the pool is category III, the water quality improvement effect of each water quality monitoring point of schemes 1–3 is shown in Figure 10. The center of the reservoir has the slowest water flow. It is the least exposed to radiation from water replenishment and quality improvement due to its unique location and distance from the intake. However, it can also be seen that the overall water quality improvement effect is significant. The average improvement rate of each water quality factor is 34.58%, close to or up to category II water standards. After water replenishment, approximately 50% of the water area in Luhun Reservoir attains level II water quality. The simulated distribution diagram of NH3-N concentration reveals a pattern of low concentration in the northern and southern regions, while the central region exhibits higher concentrations. This can be attributed to unfavorable hydrodynamic conditions in the reservoir's central area, leading to the long-term accumulation of NH3-N. In contrast, the southern entrance and northern exit of the reservoir experience more water flow exchanges and better hydrodynamic conditions, resulting in a decreasing trend of NH3-N concentration. The model simulation of TN yields similar findings, with high TN content concentrated in the middle section of the reservoir, and lower concentrations observed in the northern and southern regions. In the early stages of the ecological water replenishment project, the reservoir experienced significant external pollution, causing a substantial inflow of nitrogen that could not be degraded and absorbed promptly. Furthermore, around half of the water area in Luhun Reservoir achieves level II water quality after water replenishment. The spatial distribution simulation of TP exhibits a comparable pattern to TN, where high TP concentrations are primarily concentrated in the central region of the reservoir. Similarly, around half of the water area in Luhun Reservoir attains level II water quality after water replenishment.
Figure 10

Results of water quality improvement for schemes 1–3.

Figure 10

Results of water quality improvement for schemes 1–3.

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Scheme 4–6 results

The simulation results of schemes 4–6 are shown in Figure 11 when the initial water quality of the reservoir is category IV. The DO concentrations were 5.65, 6.22 and 5.97 mg/L, NH3-N concentrations were 1.27, 1.18 and 1.13 mg/L, TN concentrations were 1.22, 1.12 and 1.19 mg/L and TP concentrations were 0.077, 0.081 and 0.080 mg/L for the three schemes, respectively. Similar to the simulation results of scenarios 1–3, more than 50% of the water area of Luhun Reservoir after replenishment reaches Grade III water quality.
Figure 11

Water quality simulation results of schemes 4–6.

Figure 11

Water quality simulation results of schemes 4–6.

Close modal
The water quality improvement effect of each water quality monitoring point in the reservoir of schemes 4–6 is shown in Figure 12. The average improvement rate of each water quality factor is 40.22%, which is close to or reaches the standard of category III water.
Figure 12

Results of water quality improvement for schemes 4–6.

Figure 12

Results of water quality improvement for schemes 4–6.

Close modal

Water quality evaluation results

As indicated in Table 7, the water quality status in Luhun Reservoir was assessed using the single-factor index method and the integrated pollution index method for each of the six recharge scenarios. Schemes 1–3 can improve the reservoir's water quality to a cleaner state, and schemes 4–6 can cause the reservoir's water quality to become just minimally contaminated, all of which raise the Luhun Reservoir's water quality. When the initial water quality is category III, the integrated pollution index P of scheme 2 is the smallest among the three schemes, and when the initial water quality is category IV, the integrated pollution index P of scheme 5 is the smallest. It shows that under the same conditions of total water recharge, the water quality improvement effect is most significant when 0.5 times the flow rate and two times the recharge time are used for the actual recharge scheme.

Table 7

Water quality evaluation results under different water recharge schemes

SchemeSingle-factor evaluation index
Integrated pollution index PWater quality condition
DONH3-NTNTP
0.16 0.28 0.42 0.18 0.26 Cleaner 
0.09 0.26 0.38 0.20 0.23 Cleaner 
0.12 0.31 0.39 0.19 0.25 Cleaner 
0.42 0.64 0.61 0.39 0.51 Light pollution 
0.33 0.59 0.56 0.41 0.47 Light pollution 
0.37 0.57 0.60 0.40 0.48 Light pollution 
SchemeSingle-factor evaluation index
Integrated pollution index PWater quality condition
DONH3-NTNTP
0.16 0.28 0.42 0.18 0.26 Cleaner 
0.09 0.26 0.38 0.20 0.23 Cleaner 
0.12 0.31 0.39 0.19 0.25 Cleaner 
0.42 0.64 0.61 0.39 0.51 Light pollution 
0.33 0.59 0.56 0.41 0.47 Light pollution 
0.37 0.57 0.60 0.40 0.48 Light pollution 

Research comparison

The results from various scenarios consistently demonstrate significant improvements, with an average water quality enhancement rate of 40.22%, thus confirming the effectiveness of employing ecological water replenishment strategies in improving water quality. In their study, Zhai et al. (2022) investigated the water quality improvement effects of water-lifting aerator technology in regulated reservoirs. The results revealed that the adoption of water-lifting aerator technology achieved a water quality improvement rate of 21% in the reservoir. Yang et al. (2022) simulated water quality improvement effects under different replenishment scenarios during dry and wet seasons, finding that the highest improvement rate of 29.4% was attained under condition of 60% replenishment based on the average annual flow. In a study by Yang et al. (2019), various water diversion schemes were simulated to explore the impact of ecological water replenishment on water quality, revealing a maximum improvement of 28.02% in water quality parameters, with an average improvement rate of 18.28%.

  • (1)

    Construct a hydrodynamic model of the study area using an unstructured grid based on measured topographic and geographic data. Based on the hydrodynamic module, the parameters of the water quality module are adjusted by ECO Lab to establish a water quality module that can be applied to the water quality changes in the study area. The two modules are coupled to construct a coupled hydrodynamic-water quality model, and the model is simulated and validated using actual measurement data. Comparing the simulation results with the measured data, we can see that the MIKE21 model constructed in this paper can reflect the hydrodynamic and water quality changes of Luhun Reservoir more accurately.

  • (2)

    The two-dimensional hydrodynamic-water quality model was applied to simulate and analyze six different recharge schemes with varying recharge and recharge duration volumes. The results showed that various recharge schemes also affected water quality improvement. The average improvement rate of water quality in the center of the reservoir was up to 40.22%.

  • (3)

    The comprehensive pollution index method and the single-factor index method were used to evaluate the recharge effects of six scenarios. The results showed that the comprehensive pollution index of scenario 2 and scenario 5 were smaller, indicating that when the total amount of recharge is the same, the use of low flow rate and long-time ecological recharge has the most obvious improvement on the water quality of the reservoir. This study provides some theoretical support for future optimal reservoir scheduling and water resource management.

  • (4)

    This research solely focuses on the analysis from an ecological restoration perspective. In actual ecological water replenishment practices, multiple factors such as economic costs and operational scheduling conditions need to be considered comprehensively. Furthermore, this study only selected four water quality indicators and did not account for the influence of other indicators, which could potentially affect the accuracy of the simulations. Conducting research on water environmental improvement using a multi-indicator system will be a key area for future studies.

All authors contributed to the study conception and design. Writing and editing: S.G. and Y.W.; preliminary data collection: X.Z. All authors read and approved the final manuscript.

This work was supported by the Key Scientific Research Project of Colleges and Universities in Henan Province (CN) [grant number 17A570004]. The manuscript was also funded by the North China University of Water Resources and Electric Power Innovation Ability Improvement Project for Postgraduates [grant number NCWUYC-2023006].

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Al-Jawad
J. Y.
,
Alsaffar
H. M.
,
Bertram
D.
&
Kalin
R. M.
2019
A comprehensive optimum integrated water resources management approach for multidisciplinary water resources management problems
.
Journal of Environmental Management
239
,
211
224
.
Branche
E.
2017
The multipurpose water uses of hydropower reservoir: The SHARE concept
.
Comptes Rendus Physique
18
(
7–8
),
469
478
.
Cao
J.
,
Sun
Q.
,
Zhao
D.
,
Xu
M.
,
Shen
Q.
,
Wang
D.
,
Wang
Y.
&
Ding
S.
2020
A critical review of the appearance of black-odorous waterbodies in China and treatment methods
.
Journal of Hazardous Materials
385
,
121511
.
Fu
B.
,
Merritt
W. S.
,
Croke
B. F.
,
Weber
T. R.
&
Jakeman
A. J.
2019
A review of catchment-scale water quality and erosion models and a synthesis of future prospects
.
Environmental Modelling & Software
114
,
75
97
.
Gao
X.
,
Zhang
S.
,
Sun
B.
,
Li
N.
,
Liu
Y.
&
Wang
Y.
2019
Assessing the effects of restoration measures on water quality in a large shallow reservoir
.
Sustainability
11
(
19
),
5347
.
He
X.
,
Wang
H.
,
Yan
H.
&
Ao
Y.
2021
Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake
.
Water Environment Research
93
(
10
),
1934
1943
.
Herath
I. K.
,
Wu
S.
,
Ma
M.
&
Ping
H.
2022
Heavy metal toxicity, ecological risk assessment, and pollution sources in a hydropower reservoir
.
Environmental Science and Pollution Research
29
(
22
),
32929
32946
.
Huang
F.
,
Huang
Y.
,
Jia
J.
,
Li
Z.
,
Xu
J.
,
Ni
S.
&
Xiao
Y.
2022
Research and engineering application of bypass combined artificial wetlands system to improve river water quality
.
Journal of Water Process Engineering
48
,
102905
.
Li
S.
,
Feng
J.
,
Zhang
S.
,
Zhao
J.
,
Peng
W.
,
Zhu
G.
&
Yu
Z.
2020a
Simulation analysis of water quality improvement effect of ecological water replenishment measures in Qilu Lake
.
Hydropower Energy Science
38
,
35
39
.
Li
X.
,
Huang
M.
&
Wang
R.
2020b
Numerical simulation of Donghu Lake hydrodynamics and water quality based on remote sensing and MIKE 21
.
ISPRS International Journal of Geo-Information
9
(
2
),
94
.
Liang
J.
,
Yang
Q.
,
Sun
T.
,
Martin
J. D.
,
Sun
H.
&
Li
L.
2015
MIKE 11 model-based water quality model as a tool for the evaluation of water quality management plans
.
Journal of Water Supply: Research and Technology – AQUA
64
(
6
),
708
718
.
Peterson
S. A.
1982
Lake restoration by sediment removal
.
JAWRA Journal of the American Water Resources Association
18
(
3
),
423
436
.
Qu
J.
&
Fan
M.
2010
The current state of water quality and technology development for water pollution control in China
.
Critical Reviews in Environmental Science and Technology
40
(
6
),
519
560
.
Sharma
S.
&
Bhattacharya
A. J. A. W. S.
2017
Drinking water contamination and treatment techniques
.
Applied Water Science
7
(
3
),
1043
1067
.
Tang
C.
,
Yi
Y.
,
Yang
Z.
,
Zhang
S.
&
Liu
H.
2018
Effects of ecological flow release patterns on water quality and ecological restoration of a large shallow lake
.
Journal of Cleaner Production
174
,
577
590
.
Yan
Z.
,
Zhou
Z.
,
Sang
X.
&
Wang
H.
2018
Water replenishment for ecological flow with an improved water resources allocation model
.
Science of the Total Environment
643
,
1152
1165
.
Yang
W.
,
Zhang
L.
,
Zhang
Y.
,
Li
Z.
,
Xiao
Y.
&
Xia
J.
2019
Developing a comprehensive evaluation method for Interconnected River System Network assessment: A case study in Tangxun Lake group
.
Journal of Geographical Sciences
29
,
389
405
.
Yang
H.
,
Ma
N.
,
Chen
J.
,
Zhang
Z.
&
Yang
J.
2022
Effect of ecological water replenishment on river water environment in mountainous cities
.
Journal of Chongqing University
45
(
S01
),
7
.
Yasarer
L. M.
&
Sturm
B. S.
2016
Potential impacts of climate change on reservoir services and management approaches
.
Lake and Reservoir Management
32
(
1
),
13
26
.
Zhai
Z.
,
Huang
T.
&
Chen
F.
2022
Application of water-lifting aeration technology in water quality improvement of diversion reservoirs
.
China Water Supply and Drainage
38
(
8
),
7
.
Zhang
Y.
,
Li
Z.
,
Ge
W.
,
Chen
X.
,
Xu
H.
,
Guo
X.
&
Wang
T.
2021
Impact of extreme floods on plants considering various influencing factors downstream of Luhun Reservoir, China
.
Science of The Total Environment
768
,
145312
.
Zhuang
W.
2016
Eco-environmental impact of inter-basin water transfer projects: A review
.
Environmental Science and Pollution Research
23
,
12867
12879
.
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