A water level rhythm calculation method for promoting restoration of aquatic vegetation in a lake or reservoir

Abstract

The present application relates to a kind of water level rhythm calculation method for promoting lake water aquatic vegetation recovery.The present application is based on lake water aquatic vegetation growth model, carries out the analysis and controllable water level discrete of different time scale under lake water level regulation constraint condition, through the numerical test of different aquatic vegetation distribution area and biomass under the scenario of typical hydro-meteorological conditions and pollutant concentration in target period, establish vegetation recovery evaluation model, the maximum biomass and corresponding water level of last period are used as initial value, the next period simulation and evaluation are carried out, until the water level rhythm that can promote the maximum recovery of vegetation is formed all year round.The method of the present application uses restrictive water level under different needs as regulation constraint, fully considers the influence of wave, lake current and other hydrodynamic elements on plant germination and growth under different water level conditions, uses different, variable time step to simulate vegetation biomass, can form the water level regulation rhythm that can promote the maximum recovery of aquatic vegetation in controllable interval water level interval.

Description

Technical Field

[0001] This invention belongs to the field of lake water ecological restoration technology, and relates to a method for calculating water level rhythms to promote the restoration of aquatic vegetation in lakes and reservoirs. Background Technology

[0002] Aquatic vegetation, as a core element of lake and reservoir ecosystems, is crucial for maintaining the ecosystem service functions of lakes and reservoirs.

[0003] Under the influence of factors such as climate change and rising lake levels caused by human activities, aquatic vegetation in lakes and reservoirs has generally degraded. Therefore, promoting the restoration of aquatic vegetation and maintaining a healthy lake ecosystem has become an important task for the ecological restoration of key lakes and reservoirs. Water level is an important indicator of the hydrological situation of lakes and reservoirs and a key environmental factor determining the distribution of aquatic plants. Therefore, under the premise of fully considering the tasks of flood control, navigation, and water supply in lakes, formulating scientific and targeted water level rhythms for aquatic vegetation restoration is of extremely important scientific value and practical significance for restoring healthy lake and reservoir ecosystems and enhancing the comprehensive service value of lakes.

[0004] Currently, commonly used methods for calculating water level requirements for aquatic vegetation include experimental methods, hydrological analysis methods, and water level-area relationship methods. These methods all fall under statistical analysis and have significant drawbacks: ① They do not consider dynamic conditions such as waves and flow fields in different lakes, as well as changes in habitat conditions such as the supply of nutrients like nitrogen and phosphorus. Given the current situation where hydrological conditions such as inflow and outflow from lakes and reservoirs in most areas have changed significantly, and water quality has generally improved, these methods cannot incorporate changes in habitat conditions such as water quality to formulate reasonable water level rhythms; ② They only consider the distribution area of ​​aquatic vegetation, failing to reflect the true distribution situation. For example, although the distribution area of ​​aquatic vegetation in many lakes and reservoirs has not decreased significantly, the plant density and total biomass have declined considerably; ③ In terms of time scale, they use years or fixed growth periods, such as the germination period in February-March and the seedling growth period in April-May, as calculation cycles, making it impossible to flexibly, specifically, and precisely formulate water level rhythms based on the climatic conditions and plant growth rates of different regions and lakes / reservoirs. Summary of the Invention

[0005] The purpose of this invention is to provide a method for calculating water level rhythms that promote the restoration of aquatic vegetation in lakes and reservoirs.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0007] A method for calculating water level rhythms to promote the restoration of aquatic vegetation in lakes and reservoirs, the method comprising the following steps:

[0008] The highest or lowest limiting water levels of lakes and reservoirs under different needs at different times are obtained, and a control water level range is constructed. The control water level range is discretized into multiple water level values ​​based on a set step size, which serve as the simulated water level sequence for each calculation period.

[0009] A growth model for aquatic vegetation in a lake / reservoir is constructed, and its governing equations are summarized as follows:

[0010]

[0011] In the formula: For the aquatic vegetation biomass of the calculation unit; The intrinsic growth rate of aquatic vegetation; , and These are functions of the effects of air temperature, nitrogen and phosphorus nutrient concentrations, and light conditions on the growth of aquatic vegetation, respectively. For time; , Coordinates; The growth diffusion coefficient of aquatic vegetation;

[0012] Based on a coupled model consisting of a lake and reservoir aquatic vegetation growth model, a water quality model, and a hydrodynamic model, the spatial distribution of aquatic vegetation biomass under different water levels in each calculation period is simulated. During the model simulation, the goal is to maximize the total aquatic vegetation biomass in each calculation period, and the optimal water level for each period is obtained.

[0013] The optimal water level for each calculation period is obtained to form the water level rhythm that promotes the restoration of aquatic vegetation in the lake and reservoir.

[0014] In some embodiments of the present invention, the water level control range is constructed by: obtaining the highest or lowest limiting water level under different needs at different times, using the maximum value of the highest limiting water level as the upper limit and the minimum value of the lowest limiting water level as the lower limit, to construct the water level control range.

[0015] In some embodiments of the present invention, the length range of the set step size is 1% to 0.10m of the water level control range.

[0016] In some embodiments of the present invention, the method further includes analyzing the spatial distribution differences of various types of aquatic vegetation at different locations in the lake or reservoir, and setting the grid size for model simulation based on the spatial differences of plant types.

[0017] In some embodiments of the present invention, the spatial differentiation degree of different types of aquatic vegetation is quantified using the coefficient of variation, and the size of the computational grid is set according to the spatial differentiation degree of aquatic vegetation. The greater the spatial differentiation degree, the finer the computational grid is set.

[0018] In some embodiments of the present invention, during model simulation, different fixed time steps or combinations of different time steps are selected for the calculation period;

[0019] The combination of different time steps includes using different time steps for the flood season and the non-flood season, wherein the time step used for the flood season calculation is less than the time step used for the non-flood season.

[0020] In some embodiments of the present invention, during model simulation, after the current calculation period is completed, the optimal water level and the corresponding aquatic vegetation biomass of the current calculation period are used as the initial values ​​for the next period to simulate the next period.

[0021] In some embodiments of the present invention, the objective function for maximizing the total biomass of aquatic vegetation is as follows:

[0022]

[0023] In the formula: This represents the maximum normalized total biomass of aquatic vegetation. These represent different water level values ​​in the water level sequence. , and These represent the average biomass of each calculation unit for submerged plants, emergent plants, and floating-leaved plants at water level n; , and These represent the distribution areas of submerged plants, emergent plants, and floating-leaved plants at water level n, respectively. and The weights of submerged plants, emergent plants, and floating-leaved plants are respectively determined.

[0024] In some embodiments of the present invention, the weights are set based on the control target.

[0025] In some embodiments of the present invention, during model simulation, the inflow river water volume, water quality, and meteorological conditions of the reservoir corresponding to the typical high, normal, and low water conditions of the calculation period are used as model driving parameters.

[0026] The method of the present invention has the following beneficial effects:

[0027] (1) The method fully considers the different needs of lakes and reservoirs at different times (including comprehensive needs such as flood control, navigation, and water supply), and its operability is significantly enhanced. This method uses the upper and lower limit water levels under different needs as constraints, calculates the most suitable water level for aquatic vegetation restoration within the controllable range, and does not affect the function of other functions such as flood regulation and storage of lakes and reservoirs. Therefore, it is highly operable and the established water level rhythm is more scientific and reasonable.

[0028] (2) By using numerical model simulation, the constructed aquatic vegetation growth model is coupled with conventional hydrodynamic and water quality models to fully consider the erosion of vegetation substrate and mechanical damage to plants by hydrodynamic elements such as waves and lake currents under different water level conditions, as well as the influence of nitrogen and phosphorus nutrient concentration changes on plant germination and growth, so as to simulate aquatic vegetation biomass more scientifically.

[0029] (3) A water level regulation rhythm can be formed within the controllable water level range to promote the maximum recovery of aquatic vegetation. Using the maximum vegetation distribution and the corresponding suitable water level obtained in the previous period as the initial values ​​for regulation, and using a continuous time period calculation strategy for iterative calculation, the water level rhythm that is conducive to the maximum recovery of aquatic vegetation throughout the year can be obtained.

[0030] (4) Taking into account the water level requirements of different types of aquatic vegetation, and adjusting the key vegetation types to be regulated according to the differences in the ecosystem structure of the target lakes and reservoirs, it has strong flexibility and practicality. By setting up a multi-target type plant comprehensive assessment method, emergent plants, submerged plants and floating-leaved plants can be taken into account, and the vegetation types that need to be restored in the target lakes and reservoirs can be adjusted by adjusting the weight coefficients, which has strong flexibility and practicality.

[0031] (5) The temporal resolution of water level rhythm formulation is significantly improved. Based on continuous calculation using a numerical model, this method can formulate water level regulation rhythms for lakes and reservoirs at multiple scales, such as daily, weekly, ten-day, and monthly, according to the climate differences and management objectives of the lake and reservoir areas. It also supports variable scales. The temporal resolution is significantly improved compared to the current lake and reservoir scheduling map method, which has a minimum scale of ten days and a common scale of monthly. The application is also more flexible. Attached Figure Description

[0032] Figure 1 This is a flowchart for calculating the water level rhythm that promotes the restoration of aquatic vegetation in lakes and reservoirs.

[0033] Figure 2 This is a technical route for constructing a dynamic model of aquatic vegetation growth.

[0034] Figure 3 These are the initial values ​​calculated for a 25m scale model of submerged vegetation.

[0035] Figure 4 This represents the change in the total biomass (thousand tons) of aquatic vegetation in Lake A at different water levels ranging from 7.4m to 10.5m.

[0036] Figure 5 It represents the changes in the total biomass of aquatic vegetation at the beginning and end of each month in Lake A under different water levels, as well as the water level thresholds most suitable for the expansion of aquatic vegetation.

[0037] Figure 6This study uses this method to calculate the monthly water level rhythm that promotes the maximum recovery of aquatic vegetation in Lake A and compares it with the actual water level changes in Lake A from 2018 to 2022. Detailed Implementation

[0038] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. The embodiments are used to illustrate the present invention, but are not intended to limit the scope of application of the present invention; the solution of the present invention can be applied to different lakes and reservoirs.

[0039] Example 1

[0040] The calculation method and process for water level rhythm to promote the restoration of aquatic vegetation in lakes and reservoirs are as follows: Figure 1 As shown, the specific steps include the following:

[0041] Step 1: Collection and compilation of historical data and information on lakes and reservoirs

[0042] Historical hydrological, meteorological, water quality, and aquatic ecological data for the target lakes and reservoirs were collected and organized. Hydrological data included daily flow rates of rivers flowing into and out of the lakes and lake levels. Meteorological data included daily average precipitation, air temperature, surface evaporation, hourly wind field, and solar radiation from multiple stations within the watershed. Water quality data included total nitrogen, total phosphorus, ammonia nitrogen, permanganate index, dissolved oxygen, pH, nitrate nitrogen, nitrite nitrogen, orthophosphate, and chlorophyll a concentrations from multiple stations within the lake. Aquatic ecological data included the distribution area, area, and biomass of submerged plants, emergent plants, and floating-leaved plants, as well as fish biomass.

[0043] Step 2: Determining the constraints for lake and reservoir water level regulation

[0044] Analyze the highest and lowest water levels of the target lake / reservoir under different needs for flood control, water supply, navigation, and irrigation at different times to determine the water level range within which regulation can be implemented during the calculation period. Flood control water levels are generally the restricted water levels during the flood season and must not be exceeded. Water supply, navigation, and irrigation water levels must meet the minimum water level requirements for these functions. If there is no maximum water level restriction during the non-flood season, the historical highest water level of the lake / reservoir can be used as the upper limit for regulation. The controllable water level is the intersection of the needs for flood control, navigation, water supply, and irrigation in each calculation period, i.e.:

[0045]

[0046] In the formula: WT is the adjustable water level range; The maximum water level is the flood control limit for lakes and reservoirs. Minimum water level for navigation restrictions. Water supply limit level (minimum water level) and The minimum water level is the water level required for irrigation.

[0047] Step 3: Determine the optional calculation period

[0048] Based on regional climate conditions and the life history of aquatic vegetation, different time steps such as weekdays, ten-day periods, half-months, months, or quarters, or combinations of different time steps, can be selected for the calculation period. For example, a ten-day scale can be used during the flood season, and a monthly scale can be used during the non-flood season.

[0049] Step 4: Discretization of adjustable water levels in lakes and reservoirs and scenario design

[0050] Using the minimum and maximum values ​​of the controllable water level range determined in step 2 as the limiting water levels, and setting the step size for raising the water level based on the minimum water level, the water level is raised sequentially to the maximum water level, resulting in a set of water level control scenarios. The lake and reservoir water levels are discretized according to the range of 1% to 0.10m of the controllable range. For example, if the controllable water level range of a certain lake is between 3.0m and 5.0m, the water level can be discretized according to the range of 0.02m to 0.10m, generating 20 to 100 water level control scenarios.

[0051] Step 5: Construction of a growth model for aquatic vegetation in lakes and reservoirs

[0052] This study analyzes the key influencing factors and processes affecting the germination, growth, and death of submerged, emergent, and floating-leaved plants in lakes and reservoirs. It generalizes mathematical expressions, quantifies key model parameters, develops a numerical discretization algorithm, formulates a model calculation program, and conducts model parameter calibration and validation. The governing equations for the growth of submerged, emergent, and floating-leaved plants in lakes and reservoirs can be generalized as follows:

[0053]

[0054] In the formula: To calculate the biomass of aquatic vegetation in a unit (kg / m²) 2 ); Intrinsic growth rates of different types of aquatic vegetation; , and The functions representing the effects of air temperature, nitrogen and phosphorus nutrient concentrations, and light conditions on the growth of various types of aquatic vegetation were obtained through literature review and preliminary experimental methods, and were finally determined through parameter calibration. For time; , Coordinates; The growth diffusion coefficient represents the growth diffusion coefficient of different types of aquatic vegetation.

[0055] Currently, the numerical models of aquatic vegetation dynamics in lakes and reservoirs treat aquatic vegetation as a large category for calculation, or only consider submerged vegetation. This invention, based on the differences in vegetation types in different lakes and reservoirs, subdivides vegetation into emergent, floating-leaved, and submerged vegetation for separate calculation through parameter differences, significantly improving the level of refinement and making it applicable to lakes and reservoirs with different vegetation cover types.

[0056] Step 6: Generation of differentiated and refined initial field based on the spatial characteristics of aquatic vegetation

[0057] The spatial distribution differences of various types of aquatic vegetation at different locations in lakes and reservoirs were analyzed. Plant types with small spatial differences were assigned a relatively coarse computational grid, while plant types with large spatial differences were assigned a relatively fine computational grid.

[0058] Specifically, the spatial differentiation of different types of vegetation is quantified using the coefficient of variation; for indicators with small spatial differentiation, orthogonal grids of 250m to 1000m can be used for calculation, while for indicators with large spatiotemporal differentiation, orthogonal grids of 25m can be used for calculation.

[0059] Step 7: Selecting Typical Years and Generating Water Quality Scenarios for Inflow and Outflow Rivers

[0060] Using the long-series data of lake and reservoir inflow and outflow river runoff or watershed precipitation obtained in step 1, frequency analysis is carried out to determine typical years of abundant, normal, and dry water conditions. The hydrological frequency analysis can adopt the fitting method, and the years corresponding to the exceedance probabilities of 25%, 50%, and 75% can be selected as typical years of abundant, normal, and dry water conditions. The water volume and quality of rivers flowing into the lake and reservoir in typical years, as well as temperature, precipitation, and wind field, are used as the driving functions of the model.

[0061] Step 8: Numerical Experiment on the Evolution of Different Types of Aquatic Vegetation in Lakes and Reservoirs under the Influence of Hydrodynamic and Water Quality Changes

[0062] A coupled model of lake / reservoir aquatic vegetation growth, hydrodynamics, and water quality is constructed to simulate the growth of different types of aquatic plants. Mature hydrodynamic and water quality models already exist in existing technologies. In this embodiment, the lake / reservoir aquatic vegetation growth model is coupled to the existing three-dimensional hydrodynamic and water quality model EcoLake to form a hydrodynamic, water quality, and aquatic ecosystem model capable of simulating the growth of different types of aquatic plants. The hydrodynamic, water quality, and aquatic ecosystem model is driven by river flow scenarios and pollutant concentration scenarios in different zones to perform numerical simulations of the spatial distribution and biomass evolution of submerged plants, floating-leaved plants, and emergent plants, and the calculation results are saved. This step is repeated until all water level scenarios for that time period are simulated.

[0063] Step 9: Calculate the comprehensive assessment of aquatic vegetation and optimal water level in the lake / reservoir during the calculation period.

[0064] A comprehensive assessment model for aquatic vegetation status was established, using submerged vegetation biomass, emergent vegetation biomass, and floating-leaved plant biomass as evaluation indicators. This model calculated and assessed the biomass of different types of aquatic vegetation and the distribution of all aquatic vegetation in the lake / reservoir under different water level scenarios for a given time period, determining the optimal water level for vegetation restoration during that period. The water level corresponding to the maximum normalized total biomass of submerged, emergent, and floating-leaved aquatic vegetation in the lake / reservoir was selected as the preferred target.

[0065]

[0066] In the formula: This represents the maximum normalized total biomass of aquatic vegetation. For scenarios at various water levels, , and The average biomass (kg / m³) of each computational unit output by the models for submerged plants, emergent plants, and floating-leaved plants under water level scenario n are shown below. 2 ); , and These represent the three types of vegetation distribution areas obtained statistically from each computational grid of the model, i.e., the total area (m²) of the grids with biomass greater than 0 in the computational unit. 2 ); , and These represent the weights of the three plant species, i.e., the importance coefficients of the three plant species for different lakes. , and The specific requirements can be set according to the control objectives. For most lakes and reservoirs, the distribution of submerged and emergent vegetation is more important for the healthy ecosystem of the lake or reservoir, followed by floating-leaved plants. , and The values ​​were assigned to 0.4, 0.4, and 0.2 respectively; for specific lakes and reservoirs requiring special control, if only submerged plants are needed, then... and Set the value to 0.

[0067] Step 10: Simulation and analysis to determine the optimal water level for the next time period

[0068] Using the optimal water level of the previous period and the spatial distribution of biomass of various types of plants at the end of the period below the optimal water level obtained in step 9 as initial values, repeat step 8 to carry out numerical experiments on the evolution of different types of aquatic vegetation in lakes and reservoirs under all water level scenarios, and obtain the optimal water level that is conducive to vegetation recovery in the next period, until all calculation periods included in the whole year have been calculated.

[0069] Step 11: Calculation of water level rhythms conducive to the maximum recovery of aquatic vegetation

[0070] To obtain the most suitable water level for vegetation restoration at all times throughout the year; and to establish a lake and reservoir water level rhythm that is conducive to the maximum restoration and expansion of aquatic vegetation throughout the year.

[0071] Example 2

[0072] This embodiment takes the formulation of a monthly water level rhythm that can maximize the restoration of aquatic vegetation in Lake A as an example to further describe the technical solution of the water level rhythm calculation method for promoting the restoration of aquatic vegetation in lakes and reservoirs according to the present invention. The solution is also applicable to other scales such as ten-day periods and seasons.

[0073] Lake A is a freshwater lake with a water area of ​​769.6 km². 2 The average water level over many years is 8.5m, the average water depth is about 2.7m, and the volume is 2.07 billion cubic meters. 3 It simultaneously serves multiple functions, including flood control, navigation, water supply, ecology, and tourism. Lake A's drainage area is 13,486 km². 2 The drainage area of ​​Lake A is 9153 km². 2 Lake A has numerous tributaries, with the main stream and tributaries mostly tree-like, and clear hydraulic connections. Before 1960, the aquatic plant cover of Lake A was approximately 30%, but it dropped to below 5% by the late 1970s, and is now less than 1%. Therefore, promoting the restoration of aquatic vegetation in lakes and reservoirs and maintaining a healthy lake ecosystem has become an important task in the ecological restoration of key lakes and reservoirs. The method of this invention is used to calculate the water level rhythm for promoting aquatic vegetation restoration in Lake A. The specific steps are as follows:

[0074] (1) Collection and collation of historical data of Lake A

[0075] A relatively complete hydrological, hydrodynamic, meteorological, and water quality monitoring network has been established in the A Lake basin, covering the entire basin and the lake itself. Real-time data and some historical data can be queried and downloaded through the corresponding monitoring system, mainly including:

[0076] 1) Hydrological Data: Multiple hydrological and flow monitoring stations have been established in the watershed; automatic flow monitoring stations have been successively built at the main river inflow sections of Lake A since 2019, with a monitoring frequency of 5 minutes; in previous work, the applicant team built a Sontek SL500 high-frequency acoustic Doppler automatic flow meter at the inflow points of some rivers flowing into Lake A, with a monitoring frequency of 10 minutes. Using the monitoring data from the aforementioned hydrological stations, flow monitoring stations, and flow meters, hydrological and flow monitoring data for the Lake A watershed can be obtained.

[0077] 2) Water level data: Four water level stations have been built in the lake area, and daily water level data of each station since 1998 have been obtained.

[0078] 3) Meteorological data: Basic data such as daily precipitation, temperature, wind speed and direction from multiple meteorological stations within the basin since 1953 were obtained. In addition, more than 200 automatic rain gauges have been established in the basin, with a monitoring frequency of 1 hour. Multiple automatic weather stations have been built in the lake area, and the monitoring indicators include temperature, precipitation, 10-minute wind speed and direction, air pressure, solar radiation and humidity.

[0079] 4) Water Quality Data: Since 2001, water quality monitoring has been conducted at 13 major river inflow sections and 14 monitoring points within the lake area of ​​Lake A. Furthermore, since 2018, multiple automatic water quality monitoring stations have been constructed in batches throughout the Lake A basin. Automatic water quality monitoring stations, either house-type or cabin-type, have been installed at several major river inflow sections and multiple monitoring points within the Lake A area. Monitoring indicators include water temperature, pH, dissolved oxygen, conductivity, turbidity, total nitrogen, total phosphorus, ammonia nitrogen, and permanganate index. Multiple water quality stations within the lake area provide chlorophyll a and algal density monitoring data, with monitoring frequencies ranging from 1 hour to 4 hours.

[0080] (2) Analysis of water level regulation constraints and water level discretization method of Lake A

[0081] Lake A serves multiple functions, including flood control, navigation, and water supply. For flood control, the water level is required to not exceed 8.5m during the flood season from May to September; for navigation, the minimum navigable water level is designed to be no less than 7.4m; and for water supply, the minimum is also no less than 7.4m. Taking into account the actual water level control of Lake A, as well as the actual needs for navigation and water supply, the design minimum water level for Lake A is 7.4m, increasing linearly to 10.5m in 0.1m increments, generating 32 different water level scenarios for each time period.

[0082] (3) Construction of a growth dynamics model for aquatic vegetation

[0083] First, the plant community and key processes to be simulated are determined and their mathematical expressions are established. Based on the measurement results of the distribution and physiological and ecological parameters of aquatic plants in Lake A in this project, the values ​​of key model parameters such as intrinsic growth rate and initial values ​​of the model are determined. Then, the above process is transformed into a program and coupled into the EcoLake model. External functions, boundary conditions, initial conditions, etc. are added to calibrate and verify the model parameters. The basic process of constructing the aquatic plant growth model of Lake A is as follows: Figure 2 As shown.

[0084] (4) Generation of differentiated and refined initial field based on the spatial characteristics of aquatic vegetation

[0085] Orthogonal grids were used to partition Lake A into computational units. To improve model efficiency, a differentiated computational space partitioning strategy was adopted, taking into account the spatiotemporal differences in the distribution of different elements: the spatial distribution differences in emergent plant biomass and density were relatively small, and the horizontal spatial resolution of the computational grid was divided into 250m units. The lake has a grid size of 250m, comprising 12,513 horizontal computational units. Due to the small area of ​​submerged vegetation and significant spatial variations in plant density, a 250m grid is insufficient for refined simulation. Therefore, a 25m × 25m grid was used to divide the computational space for the plant growth model. Using a combination of Google Maps and manual field surveys, high-performance servers were employed to generate 25m × 25m aquatic vegetation cover data for Lake A, dividing the entire lake into 1.248 million computational grids. Figure 3This provides a high-resolution boundary for calculating the germination and growth model of aquatic plants. The model's time calculation step size is determined to be 15 seconds based on the numerical calculation convergence condition of the computational unit partitioning.

[0086] (5) Typical hydrological conditions and external function calculation

[0087] Using the daily precipitation observation data from multiple basic meteorological stations since 1953 as described in step (1), the daily data are first summed to obtain the total monthly precipitation, and the arithmetic mean is taken as the monthly average precipitation of the basin. Then, the total precipitation of different months in each year is arranged in ascending order, and a cumulative probability distribution map of the total monthly precipitation is plotted to determine the monthly precipitation corresponding to different guarantee rates, as shown in Table 1. The same calculation method is used for different time scales such as decadal and seasonal precipitation.

[0088] Table 1. Total monthly precipitation (mm) corresponding to different guarantee rates for Lake A in each month.

[0089]

[0090] (6) Numerical experimental method for the evolution of aquatic vegetation at different water levels

[0091] Taking the average water level in each month as an example, an inflow-driven model was estimated based on meteorological conditions of Lake A, water quality indicators of the rivers flowing into the lake, and a model to simulate the response characteristics of total submerged plant biomass in Lake A under different water level scenarios. During the simulation, to maintain the water level of Lake A at a specific level, the outflow from the Lake A sluice gate was set to be consistent with the total inflow from the surrounding rivers. To improve simulation efficiency, a monthly optimization simulation method was adopted, i.e., simulations were performed month by month starting in January, with the maximum submerged plant biomass of Lake A at different water levels obtained in the previous month's simulation used as the initial value for the following month's simulation. This simulation method reduces the simulation space by one dimension, improving simulation efficiency. Furthermore, since the optimal water level obtained in the previous month is used as the initial value each month, the final simulation results ensure a continuous monthly water level process that maximizes the expansion of aquatic plants in Lake A.

[0092] (7) Comprehensive assessment of aquatic vegetation and optimal water level in lakes and reservoirs during the calculation period

[0093] Under normal water conditions each month, and based on measured meteorological conditions and model estimations, the response characteristics of total submerged plant biomass (tons) in Lake A from January to December under a water level regulation scenario of 7.4m~10.5m driven by inflow are as follows: Figure 4As shown in the figure, the horizontal axis represents the daily process of each month, and the vertical axis represents the change in total biomass as the water level rises. The color bands transitioning from green to yellow to red indicate a decrease in total biomass, while those transitioning from green to blue to purple indicate an increase in biomass. The figure shows that the growth process of submerged plants in Lake A varies with water level changes across months. January is the month with the lowest average temperature in the Lake A watershed, coinciding with the plant overwintering season. Model simulation results indicate that under different water level control scenarios, the total biomass of submerged plants in Lake A decreases in January compared to the beginning of the month. However, maintaining a relatively high water level helps maintain biomass. When the water level of Lake A gradually rises from 7.4m to 9.5m, both the total biomass at the end of the month and the monthly average total biomass of submerged plants in Lake A show a gradual upward trend, meaning the decrease in total biomass compared to the beginning of the month is reduced. At a water level of 7.4m, the total biomass decreases from approximately 106.3 tons at the beginning of the month to 94.7 tons at the end of the month; at a water level of 9.5m, it decreases to 98.4 tons. When the water level exceeds 9.5m, the decrease in total biomass at the end of the month gradually widens.

[0094] (8) Calculation of water level rhythm for maximum restoration of aquatic vegetation

[0095] Figure 5 The graph shows the changes in the total biomass of submerged plants in Lake A at the beginning and end of each month under different water levels. The light red bars represent the total biomass of submerged plants in Lake A at the beginning of the month, while the blue bars represent the changes in the total biomass of submerged plants in Lake A at the end of the month as the water level gradually rises. If the length of the blue bar is greater than that of the red bar, it indicates that the submerged plants in Lake A are in an expansion phase at that water level; otherwise, if the biomass at the end of the month is lower than that at the beginning of the month, it indicates that the submerged vegetation is in a shrinking phase. Figure 5 This allows us to determine the limiting water level thresholds for the expansion of aquatic vegetation in Lake A each month, with specific values ​​given in the upper right corner of the figure.

[0096] from Figure 5 As can be seen, although the total biomass of submerged plants in Lake A was declining and shrinking under all water level scenarios in January, the highest total biomass was observed at the end of the month at 9.5m. Furthermore, the increase in biomass at the end of the month was significantly slower during the rise from 8.5m to 9.5m compared to the rise from 7.4m to 8.5m. In February, when the water level rose, the total biomass of submerged plants in Lake A showed a linear downward trend. However, when the water level was below 8.0m, the biomass at the end of the month was higher than at the beginning of the month, indicating that the submerged vegetation was in an expansion phase. Based on this, the limiting water level threshold for the restoration of submerged vegetation in Lake A in February was determined to be no higher than 8.0m. Similarly, the suitable water levels for March to October were determined to be 8.1m, 8.6m, 8.7m, 8.1m, 8.3m, 8.3m, and 8.1m, respectively.

[0097] In November, when the water level rose from 7.4m to 7.5m, the total biomass of submerged plants decreased significantly. However, when the water level rose from 7.5m to 7.8m, the biomass at the end of the month showed a certain increase. Based on this, the suitable water level for aquatic plants in Lake A in November was determined to be 7.8m.

[0098] The simulation results for December are similar to those for January. Although the total biomass of submerged plants in Lake A decreased and shrank compared to the beginning of the month under all water level scenarios, the biomass was highest at the end of the month when the water level of Lake A was maintained at 7.9m. Based on this, the suitable water level for aquatic plants in Lake A in December was determined to be 7.9m.

[0099] Figure 6 This figure shows the monthly water level rhythm that promotes the maximum recovery of aquatic vegetation in Lake A, calculated using this method, and compares it with the actual water level changes in Lake A from 2018 to 2022. The figure shows that Lake A meets the water level requirements for aquatic vegetation recovery in January, May, and June of some years, while the water level in other months is higher than the required level.

Claims

1. A water level rhythm calculation method for promoting the restoration of aquatic vegetation in a lake or reservoir, characterized by, The method comprises the following steps: Obtaining the highest or lowest restrictive water level of the lake and reservoir at different times under different demands, constructing a regulating water level interval, discretizing the regulating water level interval into a plurality of water level values based on a set step length as a water level sequence simulated in each calculation period; A lake and reservoir aquatic vegetation growth model is constructed, and the control equation is as follows: ； wherein: is the aquatic vegetation biomass of the calculation unit; is the intrinsic growth rate of the aquatic vegetation; , and are functions of the effects of air temperature, nitrogen and phosphorus nutrient salt concentration and light conditions on the growth of the aquatic vegetation, respectively; is time; , is the coordinate; is the growth diffusion coefficient of the aquatic vegetation; Based on the coupling model composed of the lake and reservoir aquatic vegetation growth model, the water quality model and the water dynamic model, the spatial distribution of the biomass of each aquatic vegetation under different water level values in each calculation period is simulated; during the model simulation, for each calculation period, the optimal water level of each period is obtained by taking the maximization of the total biomass of the aquatic vegetation as the target; the target function of the maximization of the total biomass of the aquatic vegetation is as follows: ； wherein: is the maximum value of the normalized total biomass of aquatic vegetation; is a different water level value in the sequence of water levels; , and are the average biomass of the submerged, emergent and floating-leaved plants, respectively, in each calculation unit at water level n; , and are the distribution area of the submerged, emergent and floating-leaved plants, respectively, at water level n; and are the weights of the submerged, emergent and floating-leaved plants, respectively. The optimal water level of each calculation period is obtained to form the water level rhythm for promoting the restoration of the aquatic vegetation of the lake and reservoir.

2. The method of claim 1, wherein, The regulating water level interval is constructed in the following manner: the highest or lowest restrictive water level of the lake and reservoir at different times under different demands is obtained, the maximum value of the highest restrictive water level is taken as the upper limit, and the minimum value of the lowest restrictive water level is taken as the lower limit to construct the regulating water level interval.

3. The method of claim 1, wherein, The length of the set step length ranges from 1% of the regulating water level interval to 0.10 m.

4. The method of claim 1, wherein, The method further comprises analyzing the spatial distribution difference of each type of aquatic vegetation at different points of the lake and reservoir, and setting the size of the calculation grid during the model simulation according to the spatial difference of the plant type.

5. The method of claim 4, wherein, The spatial differentiation degree of different types of aquatic vegetation is quantified by using a discrete system, and the size of the calculation grid is set according to the spatial differentiation degree of the aquatic vegetation; the greater the spatial differentiation degree, the more fine the calculation grid is set.

6. The method of claim 1, wherein, During the model simulation, different fixed time step lengths or combinations of different time step lengths are selected for the calculation period; The combination of different time step lengths comprises using different time step lengths for the flood season and the non-flood season, wherein the time step length used in the flood season is less than the time step length used in the non-flood season.

7. The method of claim 1, wherein, During the model simulation, after the calculation of the current calculation period is completed, the optimal water level and the corresponding aquatic vegetation biomass of the current calculation period are taken as the initial values of the next period for the simulation of the next period.

8. The method of claim 1, wherein, The weight is set based on the regulation target.

9. The method of claim 1, wherein, During the model simulation, the river water quantity, water quality and meteorological conditions of the typical flood, flat and dry type water regime of the calculation period into the lake and reservoir are taken as the model driving parameters.

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