Abstract
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.
HIGHLIGHTS
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.
INTRODUCTION
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.
MATERIALS AND METHODS
Study area
Location of Luhun Reservoir.
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.
, 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:
Water quality module
, 
, 
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:
The data flow in the research of hydrodynamic and water quality mode.
Water quality evaluation methods
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.
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. Water quality assessment criteria (unit: mg/L)
| No. | Evaluation factors | Grading criteria | ||||
|---|---|---|---|---|---|---|
| Category I | Category II | Category III | Category IV | Category V | ||
| 1 | Dissolved oxygen (DO) | ≥7.5 | 6 | 5 | 3 | 2 |
| 2 | Ammonia nitrogen (NH3-N) | ≤0.15 | 0.5 | 1 | 1.5 | 2 |
| 3 | Total nitrogen (TN） | ≤0.2 | 0.5 | 1 | 1.5 | 2 |
| 4 | Total phosphorus (TP） | ≤0.01 | 0.025 | 0.05 | 0.1 | 0.2 |
| No. | Evaluation factors | Grading criteria | ||||
|---|---|---|---|---|---|---|
| Category I | Category II | Category III | Category IV | Category V | ||
| 1 | Dissolved oxygen (DO) | ≥7.5 | 6 | 5 | 3 | 2 |
| 2 | Ammonia nitrogen (NH3-N) | ≤0.15 | 0.5 | 1 | 1.5 | 2 |
| 3 | Total nitrogen (TN） | ≤0.2 | 0.5 | 1 | 1.5 | 2 |
| 4 | Total phosphorus (TP） | ≤0.01 | 0.025 | 0.05 | 0.1 | 0.2 |
The integrated pollution index is shown in Table 2.
Integrated pollution index method evaluation grading standard
| Integrated pollution index P | Water quality condition | Grading 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 P | Water quality condition | Grading 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 AND VERIFICATION
Model construction
Grid division
Grid division and topography of Luhun Reservoir.
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
- (6)
Wind forcing
Manning’ M distribution map of Luhun Reservoir.
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.
Water quality model parameter setting
| Status variables | Relevant parameters | Value | Unit |
|---|---|---|---|
| BOD | First-order degradation rate (dissolved state) | 0.46 | /day |
| Degradation temperature coefficient (dissolved state) | 1.12 | ||
| Oxygen concentration half-saturation number | 2 | mg/L | |
| DO | Maximum oxygen production rate at noon (/m2) | 2 | /day |
| Plant respiration rate (/m2) | 0.125 | /day | |
| Respiration temperature coefficient | 1.08 | ||
| Respiratory half-saturation number | 2 | mg/L | |
| Substrate oxygen demand (/m2) | 0.5 | /day | |
| Substrate oxygen demand temperature coefficient | 1.07 | ||
| Substrate oxygen demand half-saturation number | 2 | mg/L | |
| NO2 | First-order decay rate of nitrification | 0.05 | /day |
| Decay rate temperature coefficient (NH4-NO2) | 1.06 | ||
| Decay rate temperature coefficient (NO2-NO3) | 1.06 | ||
| Nitrification oxygen demand (NH4-NO2) | 3.35 | g O2/g NH3-N | |
| Nitrification oxygen demand (NO2-NO3) | 1.21 | g O2/g NO3-N | |
| Nitrification oxygen half-saturation number | 2 | 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 variables | Relevant parameters | Value | Unit |
|---|---|---|---|
| BOD | First-order degradation rate (dissolved state) | 0.46 | /day |
| Degradation temperature coefficient (dissolved state) | 1.12 | ||
| Oxygen concentration half-saturation number | 2 | mg/L | |
| DO | Maximum oxygen production rate at noon (/m2) | 2 | /day |
| Plant respiration rate (/m2) | 0.125 | /day | |
| Respiration temperature coefficient | 1.08 | ||
| Respiratory half-saturation number | 2 | mg/L | |
| Substrate oxygen demand (/m2) | 0.5 | /day | |
| Substrate oxygen demand temperature coefficient | 1.07 | ||
| Substrate oxygen demand half-saturation number | 2 | mg/L | |
| NO2 | First-order decay rate of nitrification | 0.05 | /day |
| Decay rate temperature coefficient (NH4-NO2) | 1.06 | ||
| Decay rate temperature coefficient (NO2-NO3) | 1.06 | ||
| Nitrification oxygen demand (NH4-NO2) | 3.35 | g O2/g NH3-N | |
| Nitrification oxygen demand (NO2-NO3) | 1.21 | g O2/g NO3-N | |
| Nitrification oxygen half-saturation number | 2 | 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.
Water quality parameters of Luhun Reservoir
| Status variables | BOD | DO | NH4 | NO2 | NO3 | PO4 |
|---|---|---|---|---|---|---|
| Concentration (mg/L) | 0.76 | 12.80 | 0.062 | 0.026 | 4.32 | 0.28 |
| Status variables | BOD | DO | NH4 | NO2 | NO3 | PO4 |
|---|---|---|---|---|---|---|
| Concentration (mg/L) | 0.76 | 12.80 | 0.062 | 0.026 | 4.32 | 0.28 |
Model verification
Hydrodynamic model verification
Comparison of simulated and observed water levels at Luhun hydrological station.
Relative error between simulated and observed water levels at Luhun hydrological station.
Relative error between simulated and observed water levels at Luhun hydrological station.
Water quality model verification
Comparison between simulated and observed data of water quality status variables at Luhun hydrological station.
Comparison between simulated and observed data of water quality status variables at Luhun hydrological station.
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.
Evaluation of water quality simulation results
| Evaluation indicators | DO | NH3-N | TN | TP |
|---|---|---|---|---|
| NSE | 0.847 | 0.809 | 0.913 | 0.853 |
| MAPE | 6.275% | 9.865% | 4.095% | 7.693% |
| Evaluation indicators | DO | NH3-N | TN | TP |
|---|---|---|---|---|
| NSE | 0.847 | 0.809 | 0.913 | 0.853 |
| MAPE | 6.275% | 9.865% | 4.095% | 7.693% |
WATER QUALITY IMPROVEMENT EVALUATION
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.
Water recharge scheme settings
| Scheme | Initial water quality level | Hydration flow (m3/s) | Hydration time (days) |
|---|---|---|---|
| 1 | Category III | 10 | 15 |
| 2 | 15 | 10 | |
| 3 | 30 | 5 | |
| 4 | Category IV | 10 | 15 |
| 5 | 15 | 10 | |
| 6 | 30 | 5 |
| Scheme | Initial water quality level | Hydration flow (m3/s) | Hydration time (days) |
|---|---|---|---|
| 1 | Category III | 10 | 15 |
| 2 | 15 | 10 | |
| 3 | 30 | 5 | |
| 4 | Category IV | 10 | 15 |
| 5 | 15 | 10 | |
| 6 | 30 | 5 |
Location of monitoring points in the model.
Water quality simulation results
Scheme 1–3 results
Water quality simulation results of schemes 1–3.
Results of water quality improvement for schemes 1–3.
Scheme 4–6 results
Water quality simulation results of schemes 4–6.
Results of water quality improvement for schemes 4–6.
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.
Water quality evaluation results under different water recharge schemes
| Scheme | Single-factor evaluation index | Integrated pollution index P | Water quality condition | |||
|---|---|---|---|---|---|---|
| DO | NH3-N | TN | TP | |||
| 1 | 0.16 | 0.28 | 0.42 | 0.18 | 0.26 | Cleaner |
| 2 | 0.09 | 0.26 | 0.38 | 0.20 | 0.23 | Cleaner |
| 3 | 0.12 | 0.31 | 0.39 | 0.19 | 0.25 | Cleaner |
| 4 | 0.42 | 0.64 | 0.61 | 0.39 | 0.51 | Light pollution |
| 5 | 0.33 | 0.59 | 0.56 | 0.41 | 0.47 | Light pollution |
| 6 | 0.37 | 0.57 | 0.60 | 0.40 | 0.48 | Light pollution |
| Scheme | Single-factor evaluation index | Integrated pollution index P | Water quality condition | |||
|---|---|---|---|---|---|---|
| DO | NH3-N | TN | TP | |||
| 1 | 0.16 | 0.28 | 0.42 | 0.18 | 0.26 | Cleaner |
| 2 | 0.09 | 0.26 | 0.38 | 0.20 | 0.23 | Cleaner |
| 3 | 0.12 | 0.31 | 0.39 | 0.19 | 0.25 | Cleaner |
| 4 | 0.42 | 0.64 | 0.61 | 0.39 | 0.51 | Light pollution |
| 5 | 0.33 | 0.59 | 0.56 | 0.41 | 0.47 | Light pollution |
| 6 | 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%.
CONCLUSION
- (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.
AUTHORS CONTRIBUTION
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.
FUNDING
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].
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.
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