A quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21

Abstract

The present invention discloses a quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21, comprising the following steps: determining an evaluation object, selecting a quantitative simulation area, and determining the input and output boundaries of a model's non-point sources; collecting basic hydrodynamic and water quality data and measured data in the simulation area, and performing standardization processing; importing terrain into a MIKE21 model, and building a basic lake model; calculating the current pollution load entering the lake, and generalizing non-point sources into point sources; inputting model hydrodynamic boundary conditions, and calibrating and verifying hydrodynamic parameters; inputting model water quality boundary conditions, and calibrating and verifying water quality parameters; calculating the pollution load reduced by a comprehensive lake management plan, adjusting the generalized current pollution load entering the lake, and running the model; outputting hydrodynamic and water quality simulation calculation results of main points in the comprehensive management plan, comparing current simulation data, evaluating the effectiveness of comprehensive lake management, and selecting economically feasible comprehensive management measures; the present invention not only improves the economic efficiency of the project, but also achieves the expected management goals of the lake.

Description

Technical Field

[0001] The present invention relates to the technical field of lake water environment management, and in particular to a quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21. Background Art

[0002] Current river and lake water environment management measures focus on resolving existing water environment issues, ignoring the quantitative benefits of each management measure itself. Quantitative analysis of the lake's condition after comprehensive management is rarely conducted, and even situations where excessive investment fails to achieve the desired lake management goals occur. In recent years, individual cities along the Yangtze River, such as Jiujiang and Lu'an, have invested over 10 billion yuan in water environment management. The contradiction between high water environment management investment and environmental quality goals has become increasingly prominent. [1] The current status of river and lake water ecology and water resources is still not optimistic. Due to the incomplete control of pollutants discharged into rivers and lakes, the pollution load entering lakes and rivers exceeds the carrying capacity of the water environment. There is still a long way to go to achieve the goal of "three water coordination". [2] The management of river and lake water environment is a systematic project. However, my country's traditional management model lacks a systematic concept, treating the symptoms rather than the root cause, especially for cross-regional rivers and lakes, ignoring the systematic and complete nature of water environment management. [3] , focusing on the effectiveness of single river and lake governance in a fragmented manner, and the fact that the migration patterns of river and lake water pollution are relatively complex, require an integrated governance model with multiple means coordinated and multiple measures implemented simultaneously. At present, there are many studies on river and lake water environment governance under a single governance model in China. For example, Tu Huawei et al. simulated and analyzed the impact of different water diversion conditions on the water quality of the Jinshan Lake area through a water quality model. Liu Wuyi et al. simulated the ecological water cycle process of urban rivers and lakes based on a coupling model of water body circulation and water supply in surrounding areas. However, there are few simulation studies on the integrated river and lake governance model, and there is a lack of combination with actual engineering investment. Comprehensive simulation is needed to quantitatively evaluate the effectiveness of river and lake governance and balance the investment in river and lake governance with the expected goals. Summary of the Invention

[0003] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a quantitative simulation method based on MIKE21 for evaluating the effectiveness of comprehensive lake management. It can evaluate the effectiveness of comprehensive management measures according to the expected lake management goals, reduce unnecessary investment, improve the economic efficiency of the project, and achieve the expected lake management goals.

[0004] To solve the above technical problems, the technical solution adopted by the present invention is: a quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21, which includes the following steps:

[0005] S1. Determine the assessment object, select the quantitative simulation area, and determine the model non-point source input and output boundaries;

[0006] S2. Collect basic data and measured data on hydrodynamics and water quality in the simulated area and standardize the data;

[0007] S3. Construct the lake bottom terrain, import the terrain into the MIKE21 model, and build a basic lake model;

[0008] S4. Determine the catchment area of ​​the lake, calculate the current pollution load entering the lake, and generalize non-point sources to point sources;

[0009] S5. Set the model hydrodynamic parameters, set the input and output point sources, set the hydraulic structures, input the model hydrodynamic boundary conditions, and calibrate and verify the hydrodynamic parameters;

[0010] S6. Set the model water quality parameters, input point source water quality data, input model water quality boundary conditions, and calibrate and verify the water quality parameters;

[0011] S7. Based on the comprehensive lake management measures, calculate the pollution load reduction of the comprehensive lake management plan, adjust the generalized current pollution load entering the lake, and run the model;

[0012] S8. Output the simulation calculation results of hydrodynamics and water quality at the main points of the comprehensive treatment plan, compare the current simulation data, evaluate the effectiveness of the comprehensive treatment of lakes, and select economically feasible comprehensive treatment measures.

[0013] Furthermore, the step S2 specifically includes:

[0014] S2.1. Collect basic data on hydrodynamics and water quality in the simulation area, including lake bottom topography, lake water level and storage capacity, lake roughness, location and size of water intakes, pipe network inflow and outflow locations, river network inflow and outflow locations, land use type and elevation, and location and size of hydraulic structures such as weirs, culverts, and gates;

[0015] S2.2. Collect measured data for the simulation area, including water quality data for the assessment section, historical rainfall data, calibrated and verified hydrodynamic and water quality data, sediment pollutant release data, wind field data, etc.

[0016] S2.3. Standardize the collected data and convert it into the data format of the MIKE21 model, which is a sequence file with values ​​changing over time, so that it can be directly imported into the model.

[0017] Furthermore, the step S3 specifically includes:

[0018] S3.1. Based on the lake bottom topography data collected in step S2.1 and the model non-point source input and output boundaries determined in step S1, construct a lake topography file using the MIKE21 model software and import it into the MIKE21 model;

[0019] S3.2. Set the number of model simulation steps, simulation step length, and simulation base year start time to complete the construction of the lake basic model.

[0020] Furthermore, the step S4 specifically includes:

[0021] S4.1. The lake surface catchment area is determined based on the elevation data collected in step S2.1 and the ArcGIS watershed hydrological analysis.

[0022] S4.2. Based on the calculation method of pollution source discharge into the river, calculate the total amount of pollution load discharged into the lake in the simulated base year within the catchment area based on the lake surface water catchment area determined in step S4.1. Conventional water quality indicators include COD Cr , BOD5, NH3-N, TN, TP;

[0023] S4.3. Generalize the non-point source pollution load in step S4.2 to the pipe network outlet into the lake in step S2.1, and generalize it with reference to the measured flow and water quality data of the outlet to obtain the generalized outlet hydrodynamic and water quality data.

[0024] Furthermore, the step S5 specifically includes:

[0025] S5.1. Input the lake roughness n value collected in step S2.1 into the MIKE21 model. If no lake roughness n is available, set a lake roughness n. n is the calibrated hydrodynamic parameter. Input the measured data such as rainfall and wind field processed in step S2.3.

[0026] S5.2. Set the locations of point sources (such as the water intake and pipe network outlets in step S2.1) in the MIKE21 model. Input the flow rates of the corresponding point sources based on the generalized hydrodynamic data from step S4.3. Set the hydraulic structures collected in step S2.1 and input relevant information such as their locations, control water levels, and flow rates.

[0027] S5.3. Set the initial hydrodynamic conditions to the constant lake water level; input the non-point source hydrodynamic boundary data constructed in step S1, i.e., the flow or water level of the lake's outflow and inflow tributaries. To ensure the stability of the model operation, input the flow data upstream and the water level data downstream;

[0028] S5.4. Select the output type of the model hydrodynamic results as point. Set the hydrodynamic rate fixed point according to the measured data collected in step S2.2. Select the output item as lake water level or lake surface flow velocity. Set the output frequency according to actual needs and set it to an integer greater than 0 and less than or equal to the number of simulation steps. After setting, run the model to obtain the model rate fixed point hydrodynamic simulation data.

[0029] S5.5. Calibrate the model hydrodynamic calibration simulation data from step S5.4 with the measured hydrodynamic data at the lake rate fixed point in step S2.2. If the relative error R1 is within 30% and the simulated and measured hydrodynamic data have the same trend over time, the lake roughness n value set in step S5.1 is used for the next parameter verification. If the relative error R1 is greater than 30%, adjust the lake roughness n value set in step S5.1, return to step S5.4 to run the model, and repeat step S5.5 until the relative error R1 is less than or equal to 30%.

[0030] S5.6. Keep the calibrated lake roughness n unchanged, select a point different from the calibration point in step S5.4 for verification, run the model as in step S5.4, obtain the hydrodynamic simulation results at the model verification point, and perform parameter verification against the measured hydrodynamic data at the lake verification point in step S2.2. If the relative error R2 is within 30%, and the simulated and measured hydrodynamic data show consistent trends over time, the verified lake roughness n is reasonable and reliable and can be used for hydrodynamic simulation analysis of the lake water environment. If the relative error R2 is greater than 30%, adjust the lake roughness n set in step 5.1, return to step S5.4 to run the model, and repeat steps S5.5 and S5.6 until the relative error R2 is less than or equal to 30%.

[0031]

[0032] Where: R is the relative error.

[0033] Furthermore, the step S6 specifically includes:

[0034] S6.1. Import the preset water quality template into the ECO Lab water quality module of the model, set the diffusion coefficient k of each water quality indicator in the model, and input the water quality data generalized in step S4.3 into the model corresponding to the point source set in step S5.2;

[0035] S6.2. Set the initial water quality condition to the lake water quality data or water quality target value at the end of the year before the simulation base year, and input the water quality boundary data corresponding to the non-point source hydrodynamic boundary in step S5.3;

[0036] S6.3, select the output type of the model water quality result as point, the water quality rate fixed point is set according to the measured data collected in step S2.2, and the output item is the water quality data used for calibration verification, which is the conventional water quality index of step S4.2 including COD Cr , BOD5, NH3-N, TN, TP, the output frequency is set according to actual needs, which is an integer greater than 0 and less than or equal to the number of simulation steps. After setting, run the model to obtain the model rate fixed point water quality simulation data;

[0037] S6.4, calibrate the parameters of the model water quality calibration simulation data in step S6.3 with the measured water quality data of the lake calibration point in step S2.2. If the certainty coefficient R 2 If the coefficient of certainty R is greater than or equal to 0.85 and the simulated and measured water quality data have the same trend over time, the diffusion coefficient k value set in step S6.1 is used for the next parameter verification. 2 If the value of the diffusion coefficient k set in step S6.1 is less than 0.85, then return to step S6.3 to run the model and repeat step S6.4 until the coefficient of certainty R 2 Greater than or equal to 0.85;

[0038] S6.5. Keep the diffusion coefficient k unchanged and select a point different from the one determined in step S6.3 for verification. Run the model in the same way as step S6.3 to obtain the water quality simulation results of the model verification point. Verify the parameters with the measured water quality data of the lake verification point in step S2.2. If the certainty coefficient R 2 If the coefficient of certainty R is greater than or equal to 0.85 and the simulated and measured water quality data have the same trend over time, the verified diffusion coefficient k value is reasonable and reliable and can be used for lake water environment water quality simulation analysis. 2 If the value of the diffusion coefficient k set in step S6.1 is less than 0.85, then adjust the diffusion coefficient k value set in step S6.1, return to step S6.3 to run the model, and repeat steps S6.4 and S6.5 until the deterministic coefficient R 2 Greater than or equal to 0.85;

[0039]

[0040] Where: To monitor the actual water quality concentration value; The actual average value of monitored water quality concentration; is the simulated water quality concentration value; is the average value of simulated water quality concentration.

[0041] Furthermore, the step S7 specifically includes:

[0042] S7.1. Calculate the water quality reduction W for each comprehensive lake management measure based on the lake's comprehensive management measures. Adjust the outfall hydrodynamic and water quality data from steps S4.3 and S5.2, run the model, and obtain lake model simulation data after each management measure is implemented.

[0043] S7.2. The calculation and generalization principles are the same as step S7.1, and the lake model under different comprehensive management measures is run.

[0044] Furthermore, the step S8 specifically includes:

[0045] S8.1. Output the hydrodynamic and water quality simulation results from the model runs in steps S7.1 and S7.2. The output type, output items, and output frequency are the same as those in steps S5.4 and S6.3. When the output type is point, select the locations of the lake assessment section and key sections to obtain the hydrodynamic and water quality simulation results under various lake treatment measures and different comprehensive treatment measures.

[0046] S8.2. Compare the simulation results after lake treatment in step S8.1 with the simulation results for the current state of the lake at the same location in steps S5.6 and S6.5. Output the results as a point-by-point table to calculate the improvement rate θ of each water quality indicator for the assessment section and key sections after lake treatment. Output the results as a surface-by-surface chart to show the changes in hydrodynamics and the overall improvement in each water quality indicator after lake treatment.

[0047]

[0048] Where: θ represents the improvement rate of water quality index after lake treatment; c1 represents the simulation result of lake current water quality; c2 represents the simulation result of lake water quality after treatment;

[0049] S8.3. Analyze the simulation calculation results of step S8.2. If the simulation results after lake management do not meet the set expected goals, other projects can be added to re-combine the management measures, return to step S7.1, and repeat the subsequent steps until the expected goals set for the lake are achieved. Evaluate the lake benefits generated by different comprehensive management measures, and select economically feasible comprehensive lake management measures based on the engineering investment of different management measures.

[0050] Furthermore, the evaluation object in step S1 is the entire lake, and the quantitative simulation area is the entire lake water area. According to the actual distribution of project scope, assessment sections, basic data, etc., the quantitative simulation area extends to the tributaries out of the lake and the tributaries in to the lake.

[0051] Furthermore, the non-point source input and output boundaries of the model in step S1 refer to boundaries that cannot be generalized as point sources, which are the tributaries out of the lake and the tributaries in to the lake, and need to be determined before constructing the lake bottom topography.

[0052] Beneficial effects of the present invention: The present invention simulates various lake management measures and the hydrodynamics and water quality of lakes under different comprehensive measures, quantitatively analyzes the benefits generated by different comprehensive management plans, evaluates the effectiveness of comprehensive management measures according to the expected lake management goals, reduces unnecessary investment, improves the economic efficiency of the project, and achieves the expected lake management goals. It solves the problems of unsatisfactory management results caused by the fragmentation of lake projects, excessive investment but insignificant management results, etc. It is conducive to guiding the coordination of urbanization development and lake water environment systems, and can provide strong support for balancing river and lake management investment and expected goals, provide reasonable suggestions for lake water environment management, and provide reference for the analysis and management of complex lake water environments. The invention results have important scientific theoretical significance and practical application value for the efficient use of urban water environment management funds, improving the effectiveness of lake water environment management, and ensuring the long-term health of lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Construct a boundary diagram for a numerical model of a lake water environment;

[0054] Figure 2 Construct a boundary diagram for another numerical model of lake water environment;

[0055] Figure 3 Determine the water level rate for the lake model;

[0056] Figure 4 This is a lake model water level verification map;

[0057] Figure 5 COD for lake models Cr Rating chart;

[0058] Figure 6 COD for lake models Cr Verification diagram

[0059] Figure 7 A comparison chart of simulation results under different comprehensive management measures. DETAILED DESCRIPTION

[0060] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0061] Example 1: A quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21, comprising the following steps:

[0062] 1.1 The assessment object is usually the entire lake, and the quantitative simulation area is generally the entire lake water area (see Appendix Figure 1 ), according to the actual distribution of project scope, assessment sections, basic data, etc., the quantitative simulation area can also be appropriately extended to the tributaries of the lake (inlet and outlet). Figure 2 ).

[0063] 1.2 The model non-point source input (output) boundary refers to the boundary that cannot be generalized as a point source, usually the inlet (outlet) and outlet tributaries of the lake, which needs to be determined before constructing the lake bottom topography.

[0064] 2.1 Collect data on hydrodynamics and water quality in the simulation area, including but not limited to lake bottom topography, lake water level and storage capacity, lake roughness, location and size of water intakes (if any), pipe network inflow and outflow locations, river network inflow and outflow locations, land use type and elevation, and location and size of hydraulic structures such as weirs, culverts, and gates (if any).

[0065] 2.2 Collect measured data and information in the simulation area, including but not less than water quality data of the assessment section, historical rainfall data, calibrated and verified hydrodynamic water quality data, sediment pollutant release data, wind field data, etc.

[0066] 2.3 The collected data are standardized and converted into the data format of the MIKE21 model, which is a sequence file with values ​​changing over time so that it can be directly imported into the model.

[0067] 3.1 Based on the lake bottom topography data collected in step 2.1 and the non-point source input (output) boundaries determined in step 1.2, use the MIKE21 model software to construct a lake topography file and import it into the MIKE21 model.

[0068] 3.2 Set the number of model simulation steps, simulation step length and simulation base year start time to complete the construction of the lake basic model.

[0069] 4.1 The lake surface catchment area is determined based on the elevation data collected in step 2.1 and the ArcGIS watershed hydrological analysis.

[0070] 4.2 According to the calculation method of pollution source discharge into the river in "Theory and Application of Water Environmental Capacity Calculation" (Science Press), based on the surface water catchment area of ​​the lake determined in step 4.1, calculate the total pollution load discharged into the lake in the simulated base year within the catchment area. Conventional water quality indicators include COD Cr , BOD5, NH3-N, TN, TP, increase or decrease according to actual conditions.

[0071] 4.3 Generalize the non-point source pollution load in step 4.2 to the pipe network outlet into the lake in step 2.1, and refer to the measured flow and water quality data of the outlet for generalization to obtain the generalized outlet hydrodynamic and water quality data.

[0072] 5.1 Input the lake roughness n value collected in step 2.1 into the MIKE21 model. If there is no lake roughness n, you can set a lake roughness n. n is the calibrated hydrodynamic parameter. Input the measured data such as rainfall and wind field processed in step 2.3.

[0073] 5.2 Set the point source locations such as the water intake and pipe network outlet in step 2.1 in the MIKE21 model, and input the flow rate of the corresponding point source based on the generalized hydrodynamic data in step 4.3; set the hydraulic structures collected in step 2.1, and input relevant information including but not limited to location, control water level, flow rate, etc.

[0074] 5.3 Set the initial hydrodynamic conditions to the constant water level of the lake; input the non-point source hydrodynamic boundary data constructed in step 1.2, that is, the flow or water level of the lake's inflow (inflow) and outflow tributaries. To ensure the stability of the model operation, input the flow data upstream and the water level data downstream.

[0075] 5.4 Select the output type of the model hydrodynamic results as point. The hydrodynamic rate fixed point is set according to the measured data collected in step 2.2. The output item is selected as lake water level or lake surface flow velocity (flow velocity includes size and direction). The output frequency is set according to actual needs and is an integer greater than 0 and less than or equal to the number of simulation steps. After the settings are completed, run the model to obtain the model rate fixed point hydrodynamic simulation data.

[0076] 5.5 Calibrate the model hydrodynamic calibration simulation data in step 5.4 with the measured hydrodynamic data at the lake rate fixed point in step 2.2. If the relative error R1 is controlled within 30% and the change trends of the simulated and measured hydrodynamic data over time are consistent, the lake roughness n value set in step 5.1 is used for the next parameter verification. If the relative error R1 is greater than 30%, adjust the lake roughness n value set in step 5.1, return to step 5.4 to run the model, and repeat step 5.5 until the relative error R1 is less than or equal to 30%.

[0077] 5.6 Keep the calibrated lake roughness n unchanged, select a point different from the calibrated point in step 5.4 for verification, run the model in the same way as step 5.4, obtain the hydrodynamic simulation results of the model verification point, and perform parameter verification with the measured hydrodynamic data of the lake verification point in step 2.2. If the relative error R2 is controlled within 30%, and the change trend of the simulated and measured hydrodynamic data over time is consistent, then the verified lake roughness n is reasonable and reliable and can be used for hydrodynamic simulation analysis of the lake water environment. If the relative error R2 is greater than 30%, adjust the lake roughness n value set in step 5.1, return to step 5.4 to run the model, and repeat steps 5.5 and 5.6 until the relative error R2 is less than or equal to 30%.

[0078]

[0079] Where: R is the relative error.

[0080] 6.1 Import the preset water quality template into the ECO Lab water quality module of the model, set the diffusion coefficient k of each water quality indicator of the model, and input the water quality data generalized in step 4.3 into the model corresponding to the point source set in step 5.2.

[0081] 6.2 Set the initial water quality conditions to the lake water quality data or water quality target values ​​at the end of the year before the simulation base year, and input the water quality boundary data corresponding to the non-point source hydrodynamic boundary in step 5.3.

[0082] 6.3 Select the output type of the model water quality result as point. The water quality rate fixed point is set according to the measured data collected in step 2.2. The output item is the water quality data used for calibration verification, which is the conventional water quality indicators in step 4.2 including COD Cr , BOD5, NH3-N, TN, TP. The output frequency is set according to actual needs and is an integer greater than 0 and less than or equal to the number of simulation steps. After setting, run the model to obtain the model rate fixed point water quality simulation data.

[0083] 6.4 Calibrate the model water quality calibration simulation data in step 6.3 with the measured water quality data at the lake calibration point in step 2.2. If the coefficient of certainty R 2 If the coefficient of certainty R is greater than or equal to 0.85 and the simulated and measured water quality data have the same trend over time, the diffusion coefficient k value set in step 6.1 is used for the next parameter verification. 2 If the coefficient of certainty R is less than 0.85, adjust the diffusion coefficient k value set in step 6.1, return to step 6.3 to run the model, and repeat step 6.4 until the coefficient of certainty R is less than 0.85. 2 Greater than or equal to 0.85.

[0084] 6.5 Keep the calibrated diffusion coefficient k unchanged, select a point different from the calibration point in step 6.3 for verification, run the model in the same way as step 6.3, obtain the water quality simulation results of the model verification point, and verify the parameters with the measured water quality data of the lake verification point in step 2.2. If the certainty coefficient R 2 If the coefficient of certainty R is greater than or equal to 0.85 and the simulated and measured water quality data have the same trend over time, the verified diffusion coefficient k value is reasonable and reliable and can be used for lake water environment water quality simulation analysis. 2 If the coefficient of certainty R is less than 0.85, adjust the diffusion coefficient k value set in step 6.1, return to step 6.3 to run the model, and repeat steps 6.4 and 6.5 until the coefficient of certainty R is less than 0.85. 2 Greater than or equal to 0.85.

[0085]

[0086] Where: To monitor the actual water quality concentration value; The actual average value of monitored water quality concentration; is the simulated water quality concentration value; is the average value of simulated water quality concentration.

[0087] 7.1 Based on the comprehensive lake management measures, calculate the water quality index reduction W for each management measure, adjust the generalized outlet hydrodynamic and water quality data in steps 4.3 and 5.2, run the model, and obtain the lake model simulation data after the implementation of each management measure.

[0088] 7.2 The calculation and generalization principles are the same as step 7.1, and the lake model under different comprehensive management measures is run.

[0089] 8.1 Output the hydrodynamic and water quality simulation results of the model run in steps 7.1 and 7.2. The output type, output items, and output frequency are the same as those in steps 5.4 and 6.3. When the output type is point, select the locations of the lake assessment section and key sections to obtain the hydrodynamic and water quality simulation results under various lake management measures and different comprehensive management measures.

[0090] 8.2 Compare the simulation results after lake treatment in step 8.1 with the simulation results of the current status of the lake at the same point in steps 5.6 and 6.5. When the output type is point-time list, calculate the improvement rate θ of each water quality index of the assessment section and key section after lake treatment. When the output type is surface-time column chart, it shows the changes in hydrodynamics and the overall improvement of each water quality index after lake treatment.

[0091]

[0092] Where: θ represents the improvement rate of water quality indicators after lake treatment; c1 represents the simulation result of the current water quality of the lake; c2 represents the simulation result of the water quality after lake treatment.

[0093] 8.3 Analyze the simulation calculation results of step 8.2. If the simulation results after lake treatment do not meet the set expected goals, add other projects to re-combine the treatment measures, return to step 7.1, and repeat the subsequent steps until the expected goals set for the lake are achieved. Evaluate the lake benefits generated by different comprehensive treatment measures, and select economically feasible comprehensive lake treatment measures based on the engineering investment of different treatment measures.

[0094] Example 2: Take a lake in a city along the Yangtze River (hereinafter referred to as "the lake") as an example. The lake is divided into upper lake, middle lake and lower lake. There is no stable external water supply. It mainly relies on rainwater and sewage treatment plant tail water to maintain water volume. The water level of the lake is mainly regulated by the electric pumping station during the flood season. A large number of factories, enterprises and residential areas are distributed in the lake basin. The pollution load is strong and has many components. In addition, the urban pollution prevention and control measures have been weak for a long time, resulting in the deterioration of the water quality of the lake. The water quality is difficult to meet the target requirements of Class IV water. In order to improve the water environment quality of the lake, it is planned to take three engineering measures: outlet control, bottom sediment dredging, and overflow storage. Based on the MIKE21 simulation analysis of the lake management results under different comprehensive measures, a comprehensive measure with low investment and good benefits is selected to achieve the expected lake management goals while reducing investment.

[0095] The specific steps are as follows:

[0096] 1.1 The assessment object is the lake, which is a relatively closed water area, and the quantitative simulation area is the entire water area of ​​the lake.

[0097] 1.2 The lake has no stable inflow and outflow tributaries. The external water sources are mainly rainwater and sewage discharged through the direct discharge station, so the model non-point source input (outflow) boundary is not set.

[0098] 2.1 The hydrodynamic and water quality data collected for the simulation area include: lake bottom topography, lake water level, pipe network outlet into the lake, land type and elevation, and location and scale of the pumping station.

[0099] 2.2 The measured data collected in the simulation area include: water quality data of the assessment section, historical rainfall and evaporation data, calibrated and verified hydrodynamic and water quality data, and wind field data.

[0100] 2.3 The collected data are standardized and converted into the data format of the MIKE21 model, which is a sequence file with values ​​changing over time so that it can be directly imported into the model.

[0101] 3.1 Based on the lake bottom topography data collected in step 2.1, use the MIKE21 model software to construct the lake topography file and import it into the MIKE21 model Domain.

[0102] 3.2 Set the number of model simulation steps to 8760, the simulation step length to 3600s, and the simulation base year start time to 2018 / 1 / 10:00:00 to complete the construction of the basic model of the lake.

[0103] 4.1 The surface catchment area of ​​the lake was determined based on the elevation data collected in step 2.1 and the ArcGIS watershed hydrological analysis.

[0104] 4.2 According to the calculation method of pollution source discharge into the river in "Theory and Application of Water Environmental Capacity Calculation" (Science Press), based on the surface water catchment area of ​​the lake determined in step 4.1, calculate the total pollution load discharged into the lake in the simulated base year within the catchment area. Conventional water quality indicators are COD Cr Taking the lake as an example, the current COD in 2018 was calculated. Cr The total pollution load entering the lake is 4474.6t / a.

[0105] 4.3 The current COD of 4474.6t in step 4.2 Cr The pollution load entering the lake was generalized to the pipe network outlets in step 2.1. In 2018, there were 34 outlets entering the lake. The measured flow and water quality data of some outlets were used for generalization to obtain the generalized outlet hydrodynamic and water quality data.

[0106] 5.1 Set a lake roughness n in the Bed Resistance of the Hydrodynamic Module of the MIKE21 model. The n value is the Manning number 32m 1 / 3 / s, n is the calibrated hydrodynamic parameter, and the measured data of rainfall, evaporation, and wind field processed in step 2.3 are input into the model.

[0107] 5.2 In the MIKE21 model Sources, set the location of the pipe network outlet into the lake in step 2.1 and input the flow of the 34 outlets generalized in step 4.3; set the water conservancy structures collected in step 2.1. The lake has a small electric drainage station, which is generalized as a point source. Set the point source location and input the time-water level sequence file.

[0108] 5.3 Set the initial hydrodynamic condition to the constant water level of the lake at 27.5 m; according to step 1.2, the lake model does not set non-point source input (output) boundaries.

[0109] 5.4 Select the output type of the model hydrodynamic results as point, set the hydrodynamic rate fixed point WL1 at the lake power station, select the water level as the output item, and set the output frequency to 1 according to actual needs. After the settings are completed, run the model to obtain the water level simulation data S-WL1 of the lake model WL1.

[0110] 5.5 Parameter calibration is performed on the model water level simulation data S-WL1 in step 5.4 and the measured water level data M-WL1 of WL1. Figure 3 The relative error R1 of each calculated value is less than 30%, and the average relative error of each value is 1.3%. The simulated and measured water level data have the same trend over time. The lake roughness n value is 32m 1 / 3 / s is used for parameter verification in the next step.

[0111] 5.6 Maintain the specified lake roughness n value of 32m1 / 3 / s remains unchanged, and the hydrodynamic verification point WL2 is set at the center of the lake. Run the model in the same way as step 5.4 to obtain the WL2 water level simulation result S-WL2, and perform parameter verification with the WL2 measured water level data M-WL2, see the attached Figure 4 The relative error R2 of each value is less than 30%, the average relative error of each value is 0.9%, and the trend of the simulated and measured water level data over time is consistent, so the verified lake roughness n value is 32m 1 / 3 / s is reasonable and reliable and can be used for hydrodynamic simulation analysis of the lake water environment.

[0112] 6.1 Import the preset water quality template into the ECO Lab module and set the COD Cr The diffusion coefficient k is calculated. The horizontal eddy viscosity coefficient of k is 0.28. The water quality data generalized in step 4.3 are input into the model.

[0113] 6.2 Setting the initial COD of the lake model Cr The value is 30 mg / L. According to step 5.3, no non-point source water quality boundary is set for this lake model.

[0114] 6.3 Select the output type of the model water quality result as point, set the water quality rate fixed point WQ1 at the center of the lake, and select COD as the output item Cr The output frequency is set to 1 according to actual needs. After setting, run the model to obtain the COD of the lake model WQ1. Cr Simulated data S-WQ1.

[0115] 6.4 Apply the COD model from step 6.3 Cr Simulated data S-WQ1 and WQ1 measured COD Cr Data M-WQ1 is used for parameter calibration, see attached Figure 5 , calculate the coefficient of certainty R 2 is 0.89, greater than 0.85, and the simulated and measured COD Cr The data has a consistent trend over time, COD Cr The diffusion coefficient k value of 0.28 was used for the next parameter verification.

[0116] 6.5 Keep the diffusion coefficient k at 0.28, COD Cr Verification point WQ2 is set at the center of the upper lake (assessment section) of the lake. Run the model in the same way as step 6.3 to obtain the COD of WQ2. Cr Simulation results S-WQ2 and measured COD of WQ2 Cr Data M-WQ2 for parameter verification, see attached Figure 6 , calculate the coefficient of certainty R 2 is 0.86, greater than 0.85, and the simulated and measured CODCr The data have a consistent trend over time, so the verified diffusion coefficient k value of 0.28 is reasonable and reliable and can be used to calculate the COD of the lake water environment. Cr Simulation analysis.

[0117] 7.1 According to the three engineering measures to be taken for the lake, namely, outlet control (F1), sediment dredging (F2), and overflow storage (F3), the COD of each control measure is calculated. Cr The reduction amounts are 318.7t / a, 70.1t / a and 212.1t / a respectively. In this case, the lake model under a single treatment measure state is no longer run.

[0118] 7.2 Calculation and generalization principles are the same as steps 4.3 and 5.2. According to COD Cr The reduction amount is adjusted based on the hydrodynamic and water quality data of the generalized point source. Four comprehensive measures, namely Z1 (F1+F2), Z2 (F1+F3), Z3 (F2+F3), and Z4 (F1+F2+F3), are set to run the lake model under different comprehensive management measures.

[0119] 8.1 Output the water quality simulation results after running the model in step 7.2. The output type, output items and output frequency are the same as step 6.3. The output point is WQ2 as in step 6.5. The COD of WQ2 under different comprehensive treatment measures for lakes Z1, Z2, Z3 and Z4 is obtained. Cr Simulation results S1-WQ2, S2-WQ2, S3-WQ2, S4-WQ2.

[0120] 8.2 Combine the simulation results of step 8.1 with the COD of lake point WQ2 in step 6.5 Cr The current situation simulation results S-WQ2 are compared, see the attached Figure 7 , take the COD of the last step of the model run Cr Numerical calculation of the COD of the lake at point WQ2 under different comprehensive management measures of Z1, Z2, Z3, and Z4 Cr Indicator improvement rate θ.

[0121] 8.3 Analyze the simulation results of step 8.2, COD of WQ2 under treatment measures Z1, Z2, Z3, and Z4 Cr The improvement rates are 21.9%, 29.8%, 9.6% and 32.6% respectively. The expected treatment target of the lake is Class IV water. Figure 7 Among them, the Z1 and Z3 treatment measures failed to achieve the expected goals, while the Z2 and Z4 treatment measures achieved the expected goals set for the lake. According to the preliminary design data, the total project investment of Z2 and Z4 was 563.0763 million yuan and 951.1715 million yuan respectively. Therefore, the economically feasible Z2 comprehensive treatment measure was selected to reduce project investment while achieving the expected treatment goals of the lake.

[0122] The above embodiments are merely preferred technical solutions of the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention shall be the technical solutions set forth in the claims, including equivalent alternatives to the technical features of the technical solutions set forth in the claims. In other words, equivalent alternatives and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21, characterized by: It includes the following steps: S1. Determine the assessment object, select the quantitative simulation area, and determine the model non-point source input and output boundaries; S2. Collect basic data and measured data on hydrodynamics and water quality in the simulated area and standardize the data; S3. Construct the lake bottom terrain, import the terrain into the MIKE21 model, and build a basic lake model; S4. Determine the catchment area of ​​the lake, calculate the current pollution load entering the lake, and generalize non-point sources to point sources; S5. Set the model hydrodynamic parameters, set the input and output point sources, set the hydraulic structures, input the model hydrodynamic boundary conditions, and calibrate and verify the hydrodynamic parameters; S6. Set the model water quality parameters, input point source water quality data, input model water quality boundary conditions, and calibrate and verify the water quality parameters; S7. Based on the comprehensive lake management measures, calculate the pollution load reduction of the comprehensive lake management plan, adjust the generalized current pollution load entering the lake, and run the model; S8. Output the simulation calculation results of hydrodynamics and water quality at the main points of the comprehensive treatment plan, compare the current simulation data, evaluate the effectiveness of the comprehensive treatment of lakes, and select economically feasible comprehensive treatment measures.

2. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 1 is characterized in that: The step S2 specifically includes: S2.

1. Collect basic data on hydrodynamics and water quality in the simulation area, including lake bottom topography, lake water level and storage capacity, lake roughness, water intake location and size, pipe network inlet and outlet locations, river network inlet and outlet locations, land use type and elevation, and the location and size of weirs, culverts, and sluice gates. S2.

2. Collect measured data for the simulation area, including water quality data for the assessment section, historical rainfall data, calibrated and verified hydrodynamic and water quality data, sediment pollutant release data, and wind field data; S2.

3. Standardize the collected data and convert it into the data format of the MIKE21 model, which is a sequence file with values ​​changing over time, so that it can be directly imported into the model.

3. A quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 2, characterized in that: The step S3 specifically includes: S3.

1. Based on the lake bottom topography data collected in step S2.1 and the model non-point source input and output boundaries determined in step S1, construct a lake topography file using the MIKE21 model software and import it into the MIKE21 model; S3.

2. Set the number of model simulation steps, simulation step length, and simulation base year start time to complete the construction of the lake basic model.

4. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 2 is characterized in that: The step S4 specifically includes: S4.

1. The lake surface catchment area is determined based on the elevation data collected in step S2.1 and the ArcGIS watershed hydrological analysis. S4.

2. Based on the calculation method of pollution source discharge into the river, calculate the total amount of pollution load discharged into the lake in the simulated base year within the catchment area based on the lake surface water catchment area determined in step S4.

1. Conventional water quality indicators include COD Cr , BOD5, NH3-N, TN, TP; S4.

3. Generalize the non-point source pollution load in step S4.2 to the pipe network outlet into the lake in step S2.1, and generalize it with reference to the measured flow and water quality data of the outlet to obtain the generalized outlet hydrodynamic and water quality data.

5. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 4 is characterized in that: The step S5 specifically includes: S5.

1. Input the lake roughness n value collected in step S2.1 into the MIKE21 model. If a lake roughness n is not set, n is the calibrated hydrodynamic parameter. Input the rainfall and wind field measured data processed in step S2.

3. S5.

2. Set the locations of point sources (such as the water intake and pipe network outlets in step S2.1) in the MIKE21 model. Input the flow rates of the corresponding point sources based on the generalized hydrodynamic data from step S4.

3. Set the hydraulic structures collected in step S2.1 and input the locations, control water levels, and flow rates. S5.

3. Set the initial hydrodynamic conditions to the constant lake water level; input the non-point source hydrodynamic boundary data constructed in step S1, i.e., the flow or water level of the lake's outflow and inflow tributaries. To ensure the stability of the model operation, input the flow data upstream and the water level data downstream; S5.

4. Select the output type of the model hydrodynamic results as point. Set the hydrodynamic rate fixed point according to the measured data collected in step S2.

2. Select the output item as lake water level or lake surface flow velocity. Set the output frequency according to actual needs and set it to an integer greater than 0 and less than or equal to the number of simulation steps. After setting, run the model to obtain the model rate fixed point hydrodynamic simulation data. S5.

5. Calibrate the model hydrodynamic calibration simulation data from step S5.4 with the measured hydrodynamic data at the lake rate fixed point in step S2.

2. If the relative error R1 is within 30% and the simulated and measured hydrodynamic data have the same trend over time, the lake roughness n value set in step S5.1 is used for the next parameter verification. If the relative error R1 is greater than 30%, adjust the lake roughness n value set in step S5.1, return to step S5.4 to run the model, and repeat step S5.5 until the relative error R1 is less than or equal to 30%. S5.

6. Keep the calibrated lake roughness n unchanged, select a point different from the calibration point in step S5.4 for verification, run the model as in step S5.4, obtain the hydrodynamic simulation results at the model verification point, and perform parameter verification against the measured hydrodynamic data at the lake verification point in step S2.

2. If the relative error R2 is within 30%, and the simulated and measured hydrodynamic data show consistent trends over time, the verified lake roughness n is reasonable and reliable and can be used for hydrodynamic simulation analysis of the lake water environment. If the relative error R2 is greater than 30%, adjust the lake roughness n set in step 5.1, return to step S5.4 to run the model, and repeat steps S5.5 and S5.6 until the relative error R2 is less than or equal to 30%. Where: R is the relative error.

6. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 5 is characterized in that: The step S6 specifically includes: S6.

1. Import the preset water quality template into the ECO Lab water quality module of the model, set the diffusion coefficient k of each water quality indicator in the model, and input the water quality data generalized in step S4.3 into the model corresponding to the point source set in step S5.2; S6.

2. Set the initial water quality condition to the lake water quality data or water quality target value at the end of the year before the simulation base year, and input the water quality boundary data corresponding to the non-point source hydrodynamic boundary in step S5.3; S6.3, select the output type of the model water quality result as point, the water quality rate fixed point is set according to the measured data collected in step S2.2, and the output item is the water quality data used for calibration verification, which is the conventional water quality index of step S4.2 including COD Cr , BOD5, NH3-N, TN, TP, the output frequency is set according to actual needs, which is an integer greater than 0 and less than or equal to the number of simulation steps. After setting, run the model to obtain the model rate fixed point water quality simulation data; S6.4, calibrate the parameters of the model water quality calibration simulation data in step S6.3 with the measured water quality data of the lake calibration point in step S2.

2. If the certainty coefficient R 2 If the coefficient of certainty R is greater than or equal to 0.85 and the simulated and measured water quality data have the same trend over time, the diffusion coefficient k value set in step S6.1 is used for the next parameter verification. 2 If the value of the diffusion coefficient k set in step S6.1 is less than 0.85, then return to step S6.3 to run the model and repeat step S6.4 until the coefficient of certainty R 2 Greater than or equal to 0.85; S6.

5. Keep the diffusion coefficient k unchanged and select a point different from the one determined in step S6.3 for verification. Run the model in the same way as step S6.3 to obtain the water quality simulation results of the model verification point. Verify the parameters with the measured water quality data of the lake verification point in step S2.

2. If the certainty coefficient R 2 If the coefficient of certainty R is greater than or equal to 0.85 and the simulated and measured water quality data have the same trend over time, the verified diffusion coefficient k value is reasonable and reliable and can be used for lake water environment water quality simulation analysis. 2 If the value of the diffusion coefficient k set in step S6.1 is less than 0.85, then adjust the diffusion coefficient k value set in step S6.1, return to step S6.3 to run the model, and repeat steps S6.4 and S6.5 until the deterministic coefficient R 2 Greater than or equal to 0.85; Where: To monitor the actual water quality concentration value; The actual average value of monitored water quality concentration; is the simulated water quality concentration value; is the average value of simulated water quality concentration.

7. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 6 is characterized in that: The step S7 specifically includes: S7.

1. Calculate the water quality reduction W for each comprehensive lake management measure based on the lake's comprehensive management measures. Adjust the outfall hydrodynamic and water quality data from steps S4.3 and S5.2, run the model, and obtain lake model simulation data after each management measure is implemented. S7.

2. The calculation and generalization principles are the same as step S7.1, and the lake model under different comprehensive management measures is run.

8. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 7 is characterized in that: The step S8 specifically includes: S8.

1. Output the hydrodynamic and water quality simulation results from the model runs in steps S7.1 and S7.

2. The output type, output items, and output frequency are the same as those in steps S5.4 and S6.

3. When the output type is point, select the locations of the lake assessment section and key sections to obtain the hydrodynamic and water quality simulation results under various lake treatment measures and different comprehensive treatment measures. S8.

2. Compare the simulation results after lake treatment in step S8.1 with the simulation results for the current state of the lake at the same location in steps S5.6 and S6.

5. Output the results as a point-by-point table to calculate the improvement rate θ of each water quality indicator for the assessment section and key sections after lake treatment. Output the results as a surface-by-surface chart to show the changes in hydrodynamics and the overall improvement in each water quality indicator after lake treatment. Where: θ represents the improvement rate of water quality index after lake treatment; c1 represents the simulation result of lake current water quality; c2 represents the simulation result of lake water quality after treatment; S8.

3. Analyze the simulation calculation results of step S8.

2. If the simulation results after lake management do not meet the set expected goals, add other projects to reorganize the management measures, return to step S7.1, and repeat the subsequent steps until the expected goals set for the lake are achieved. Evaluate the lake benefits generated by different comprehensive management measures, and select economically feasible comprehensive lake management measures based on the engineering investment of different management measures.

9. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 1, characterized in that: The evaluation object in step S1 is the entire lake, and the quantitative simulation area is the entire lake water area. According to the project scope, assessment section, and actual distribution of basic data, the quantitative simulation area extends to the tributaries out of the lake and the tributaries in to the lake.

10. The quantitative simulation method for evaluating the effectiveness of comprehensive lake management based on MIKE21 according to claim 1, characterized in that: The non-point source input and output boundaries of the model in step S1 refer to boundaries that cannot be generalized as point sources, and are the tributaries out of the lake and the tributaries in to the lake, and need to be determined before constructing the lake bottom topography.

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