Agricultural non-point source pollution risk identification method and system suitable for lake basin

Abstract

This invention provides a method and system for identifying agricultural non-point source pollution risks in lake basins. The method includes: acquiring meteorological and crop data and inputting them into a constructed basin SWAT model; running the model monthly to obtain the runoff of each river and the concentration and output of non-point source pollutants in each river; constructing a river-lake estuary MIKE21 model to simulate the changes in lake non-point source pollutants for the current month and predict the next month, obtaining lake water quality indicators, and determining whether the lake currently meets the requirements of the lake's water function zone. If so, the risk area under the minimum reduction inflow load scenario is determined, and the basin SWAT model is called again for basin-wide agricultural non-point source pollution control. If not, the input load of each inflow river sub-basin is reduced one by one until the water quality meets the standards. Based on the basin's agricultural characteristics and meteorological and hydrological forecasts, this invention identifies and predicts key areas and time periods for agricultural non-point source pollution and proposes small-basin and segmented area management, demonstrating significant environmental and ecological benefits as well as socio-economic benefits.

Description

Technical Field

[0001] This invention relates to the field of non-point source pollution risk identification technology, specifically to a method and system for identifying agricultural non-point source pollution risks applicable to lake basins. More particularly, it relates to a method for identifying and controlling agricultural non-point source pollution risks in watersheds based on lake water quality compliance requirements. Background Technology

[0002] The state has scientifically formulated water function zoning requirements for various lakes, and has carried out overall control over the development and utilization of water resources in the basin at a macro level, so as to provide a basis for the rational utilization, protection and management of water resources, realize the sustainable use of water resources, and provide water security for the sustainable development of society and economy.

[0003] Current methods for identifying agricultural non-point source pollution risks primarily rely on distributed hydrological models or emission coefficient methods. These methods calculate the generation and emission of agricultural non-point source pollutants against the backdrop of geographical information or statistical information on planting structure of a fixed underlying surface in a watershed. The pollutants are then ranked according to the emission volume of each sub-watershed or hydrological response unit to determine the pollution risk zone. However, these methods have three main shortcomings: First, the fixed geographical information provided by distributed hydrological models ignores seasonal variations in vegetation cover and crop types resulting from crop rotation. Different crops and growth stages require different amounts of fertilizer, inevitably leading to different runoff scenarios. Second, pollution risk zones determined based on emission volumes do not consider the self-purification capacity of receiving lakes; not all pollutants discharged into lakes contribute to the cumulative deterioration of lake water quality. Third, they fail to consider the differences in the migration and transformation of non-point source pollutants entering lakes from different regions. Due to significant regional differences in lake shape, underwater topography, substrate, and aquatic plants, hydrodynamic and aquatic ecological conditions inevitably influence the migration and transformation of pollutants in different regions. Areas with large discharge volumes may have little impact on lake water quality, while some areas with low discharge loads may have a significant impact.

[0004] In summary, the method for identifying non-point source pollution risks in lake basins should be optimized by fully considering changes in the underlying surface of the watershed, the self-purification capacity and current water quality of the lakes, and combining these with the hydrodynamic and aquatic ecological characteristics of the lakes. Therefore, the management of agricultural non-point source pollution should fully consider the characteristics of water quality changes in receiving lakes, and there is an urgent need for a method and system for identifying agricultural non-point source pollution risks in lake basins that addresses the water quality compliance requirements of lakes. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the purpose of this invention is to provide a method and system for identifying agricultural non-point source pollution risks applicable to lake basins.

[0006] A method for identifying agricultural non-point source pollution risks applicable to lake basins, provided by the present invention, includes:

[0007] Step S1: Obtain meteorological and crop data;

[0008] Step S2: Construct a watershed SWAT model, using the meteorological and crop data as inputs to the watershed SWAT model, run it monthly, and calculate the runoff of each river and the concentration and output of non-point source pollutants in each river;

[0009] Step S3: Construct a MIKE21 model of the lakes, including the estuary region;

[0010] Step S4: Use the output of non-point source pollutants from each river as the boundary condition input for the MIKE21 model of the lake, simulate the changes in non-point source pollutants in the lake in the current month and predict the changes in the lake water quality index in the next month;

[0011] Step S5: Determine whether the current lake meets the requirements of the lake water function zone based on the lake water quality indicators. If yes, proceed to step S6; otherwise, reduce the input load of each inflow river sub-basin by a preset step size and proceed to step S4.

[0012] Step S6: List the cumulative minimum reduction in lake load scenarios and determine the risk areas under the scenarios;

[0013] Step S7: Call the watershed SWAT model again and set different scenarios for watershed agricultural non-point source pollution control.

[0014] Preferably, the meteorological data includes weather forecast data for the following month, which is obtained in advance by the meteorological department;

[0015] The crop data includes the planting area, rotation time, type, and underlying surface data of the crops.

[0016] The non-point source pollutants include total phosphorus and total nitrogen;

[0017] The lake water quality indicators include the concentration of non-point source pollutants;

[0018] The risk zone refers to the hydrological response unit of the sub-basin and the maximum discharge load under the scenario.

[0019] Preferably, constructing the watershed SWAT model includes:

[0020] Step S2.1: Obtain basic watershed data;

[0021] Step S2.2: Model construction, the steps of which include sub-basin division, hydrological response unit division and model sensitivity analysis, the model includes a hydrological module and a water quality module;

[0022] Step S2.3: The parameters of the hydrological module are calibrated using measured runoff data, and the parameters of the water quality module are calibrated using measured river water quality data, thereby completing the parameter confirmation.

[0023] Preferably, step S3 includes:

[0024] Step S3.1: Obtain basic data on the lake;

[0025] Step S3.2: Model construction, the steps of which include mesh generation, terrain interpolation, boundary condition setting and initial condition setting, the model includes a hydrodynamic module and a water quality module;

[0026] Step S3.3: Parameter determination. The parameters of the hydrodynamic module are calibrated using the measured water level data of the lake, and the parameters of the water quality module are calibrated using the measured water quality data of the lake sampling points.

[0027] Preferably, the basic data of the watershed includes watershed topographic data, land use data, soil data, precipitation data, and evaporation data;

[0028] Basic data on lakes include underwater topographic data, water quality data of rivers flowing into the lake, water quantity data, precipitation data, evaporation data, and wind field data.

[0029] An agricultural non-point source pollution risk identification system suitable for lake basins, provided by the present invention, includes:

[0030] Module M1: Acquires meteorological and crop data;

[0031] Module M2: Constructs a watershed SWAT model, using the meteorological and crop data as inputs to the watershed SWAT model, runs it monthly, and calculates the runoff of each river as well as the concentration and output of non-point source pollutants in each river;

[0032] Module M3: Constructs a MIKE21 model of a lake containing the estuary region;

[0033] Module M4: The output of non-point source pollutants from each river is used as the boundary condition input of the MIKE21 model for the lake, simulating the changes in non-point source pollutants in the lake in the current month and predicting the changes in the lake water quality indicators in the next month;

[0034] Module M5: Determines whether the current lake meets the requirements of the lake water function zone based on the lake water quality indicators. If yes, it triggers module M6; otherwise, it gradually reduces the input load of each inflow river sub-basin by a preset step size, triggering module M4.

[0035] Module M6: Lists the cumulative minimum reduction in lake load scenarios and identifies the risk areas under these scenarios;

[0036] Module M7: Call the watershed SWAT model again to set up different scenarios for watershed agricultural non-point source pollution control.

[0037] Preferably, the meteorological data includes weather forecast data for the following month, which is obtained in advance by the meteorological department;

[0038] The crop data includes the planting area, rotation time, type, and underlying surface data of the crops.

[0039] The non-point source pollutants include total phosphorus and total nitrogen;

[0040] The lake water quality indicators include the concentration of non-point source pollutants;

[0041] The risk zone refers to the hydrological response unit of the sub-basin and the maximum discharge load under the scenario.

[0042] Preferably, constructing the watershed SWAT model includes:

[0043] Module M2.1: Obtain basic watershed data;

[0044] Module M2.2: Model building, the modules for model building include sub-basin division, hydrological response unit division and model sensitivity analysis, the model includes a hydrological module and a water quality module;

[0045] Module M2.3: The parameters of the hydrological module are calibrated using measured runoff data, and the parameters of the water quality module are calibrated using measured river water quality data, thereby completing the parameter confirmation.

[0046] Preferably, the module M3 includes:

[0047] Module M3.1: Obtain basic lake data;

[0048] Module M3.2: Model building, the module for model building includes mesh generation, terrain interpolation, boundary condition setting and initial condition setting, the model includes a hydrodynamic module and a water quality module;

[0049] Module M3.3: Parameter determination, calibrating the parameters of the hydrodynamic module using measured water level data from the lake, and calibrating the parameters of the water quality module using measured water quality data from lake sampling points.

[0050] Preferably, the basic data of the watershed includes watershed topographic data, land use data, soil data, precipitation data, and evaporation data;

[0051] Basic data on lakes include underwater topographic data, water quality data of rivers flowing into the lake, water quantity data, precipitation data, evaporation data, and wind field data.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. This invention fully considers the characteristics of agricultural rotation in lake basins and the changes in the underlying surface of the basin.

[0054] 2. This invention fully considers the characteristics of lake water quality, hydrodynamics and aquatic ecology, making up for the shortcomings of previous agricultural non-point source pollution risk identification methods that only consider the input lake load and do not consider the lake water environment carrying capacity and environmental capacity.

[0055] 3. This invention aims to achieve the standard of lake water quality. Based on the characteristics of watershed agriculture and meteorological and hydrological forecasts, it identifies and predicts key areas and time periods of agricultural non-point source pollution, and proposes small watershed and regional control measures, which have good environmental and ecological benefits as well as socio-economic benefits. Attached Figure Description

[0056] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0057] Figure 1 This is a schematic diagram of the working method of the present invention;

[0058] Figure 2 This is a schematic diagram of the SWAT modeling process in this invention;

[0059] Figure 3 This is a schematic diagram of the MIKE21 modeling process in this invention;

[0060] Figure 4 This is a schematic diagram of the coupling and mutual feedback process between SWAT and MIKE21 in this invention. Detailed Implementation

[0061] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0062] Example 1

[0063] According to the present invention, a method for identifying agricultural non-point source pollution risks applicable to lake basins is provided, such as... Figure 1 As shown, it includes:

[0064] Step S1: Acquire meteorological and crop data. The meteorological data includes the weather forecast data for the following month, which is obtained in advance by the meteorological department. The crop data includes the planting area, rotation time, type, and underlying surface data of the crops to be planted in the following month. The planting area and rotation time are obtained in advance by the lake management department in consultation with townships and village committees, while the type and underlying surface data are obtained monthly through multispectral remote sensing images of the watershed.

[0065] Step S2: Construct a watershed SWAT model. Use the meteorological and crop data as inputs to the watershed SWAT model, run it monthly, and calculate the runoff of each river and the concentration and output of non-point source pollutants (NPPs) in each river. In other words, obtain the agricultural NPP generation and inflow into the lake for each sub-watershed and hydrological response unit. The NPPs include total phosphorus and total nitrogen. The construction of the watershed SWAT model is as follows: Figure 2 As shown, it includes:

[0066] Step S2.1: Obtain basic watershed data. The basic watershed data includes watershed topographic data, land use, soil, precipitation, evaporation, and other data.

[0067] Step S2.2: Model Construction. The model construction steps include sub-basin division, hydrological response unit division, and model sensitivity analysis. The model includes a hydrological module and a water quality module.

[0068] Step S2.3: Parameter Determination. Specifically, the parameters of the hydrological module are calibrated using measured runoff data; the parameters of the water quality module are calibrated using measured river water quality data.

[0069] Step S3: Construct a MIKE21 model of the lakes, including the estuary region. For example... Figure 3 As shown, step S3 includes:

[0070] Step S3.1: Obtain basic lake data. The basic lake data includes underwater topography, water quality of rivers flowing into the lake, water quantity data, precipitation, evaporation, wind field, and other data.

[0071] Step S3.2: Model Construction. The model construction steps include mesh generation, terrain interpolation, boundary condition setting, and initial condition setting. The model includes a hydrodynamic module and a water quality module.

[0072] Step S3.3: Parameter Determination. The parameters of the hydrodynamic module are calibrated using measured lake water level data; the parameters of the water quality module are calibrated using measured water quality data from lake sampling points.

[0073] Step S4: Use the output of non-point source pollutants from each river as the boundary condition input for the MIKE21 model of the lake, simulate the changes in non-point source pollutants in the lake for the current month and predict the changes in total nitrogen and total phosphorus for the next month, and obtain the lake water quality indicators. The lake water quality indicators include the concentration of non-point source pollutants.

[0074] Step S5: Determine whether the current lake meets the requirements of the lake water function zone based on the lake water quality indicators. If yes, proceed to step S6; otherwise, reduce the input load of each inflow river sub-basin by a preset step size and proceed to step S4 until the water quality meets the standards.

[0075] Step S6: List the cumulative minimum inflow load scenarios and determine the risk areas under these scenarios. The risk areas refer to the hydrological response units of the sub-basins and the maximum discharge load under these scenarios.

[0076] Step S7: Call the watershed SWAT model again and set different scenarios for watershed agricultural non-point source pollution control. Specifically, set different scenarios such as fertilizer reduction and wetland interception and purification, calculate the output load, and continue until the minimum reduction load required above is achieved.

[0077] Furthermore, in conjunction with the appendix Figure 4 The present invention is described in detail below:

[0078] First, basic data on the lake basin were obtained, and a SWAT model was constructed. A basic model was built using data on basin topography, land use, soil, precipitation, and evaporation, generating sub-basins and hydrological response units. The model was then calibrated and validated based on historical water quality (total nitrogen, total phosphorus) and water quantity data.

[0079] Then, through joint consultations between the lake management department and various townships and village committees in the basin, the types of crops planted and the rotation schedules for the following month are reported in advance by each township and village committee. Furthermore, multispectral remote sensing images of the basin (such as the Landsat series and Sentinel series) are acquired monthly. Based on the different reflectance characteristics of crops, different crop categories are identified, and new crop type data and underlying surface data are extracted.

[0080] Next, by obtaining the weather forecast data for the following month from the meteorological department, the SWAT model is run month by month to calculate the amount of agricultural non-point source pollutants (total nitrogen and total phosphorus) generated and entering the lake in each sub-basin and hydrological response unit in the following month.

[0081] Next, basic data on the lake were collected to construct the MIKE21 model of the estuary-lake. Data such as underwater topography of the lake, water quality and quantity of the inflowing rivers, precipitation evaporation, and wind field were used to build the basic model. The model was calibrated and validated using measured data on lake water level, biochemical oxygen demand, chlorophyll a, dissolved oxygen, total nitrogen, total phosphorus, and ammonia nitrogen.

[0082] Then, the output of pollutants (total phosphorus and total nitrogen) in each river for the current month and the following month, calculated by the SWAT model, is used as the boundary condition input for the MIKE21 model to simulate the changes in total nitrogen and total phosphorus in the lake for the current month and predict the changes for the following month.

[0083] Next, based on the total nitrogen and total phosphorus concentrations of the lake output by MIKE21, it is determined whether they meet the requirements of the lake's water function zone. If they do not meet the requirements, the input load of each river sub-basin flowing into the lake is reduced step by step in increments of 5% to 10%, and the MIKE21 model is used for simulation until the water quality meets the standards.

[0084] Next, the scenario of cumulative minimum reduction in lake load is listed, and its river sub-basin is determined. The hydrological response unit of the sub-basin and the maximum discharge load is the risk zone.

[0085] Finally, the SWAT model is called again, and different scenarios such as fertilizer reduction and wetland interception and purification are set to calculate the output load until the minimum reduction load required above is reached. The scenario conditions for load reduction are the effective control measures for agricultural non-point source pollution.

[0086] Example 2

[0087] The present invention also provides an agricultural non-point source pollution risk identification system suitable for lake basins. The agricultural non-point source pollution risk identification system suitable for lake basins can be implemented by executing the process steps of the agricultural non-point source pollution risk identification method suitable for lake basins. That is, those skilled in the art can understand the agricultural non-point source pollution risk identification method suitable for lake basins as a preferred embodiment of the agricultural non-point source pollution risk identification system suitable for lake basins.

[0088] An agricultural non-point source pollution risk identification system suitable for lake basins, provided by the present invention, includes:

[0089] Module M1: Acquires meteorological and crop data. Meteorological data includes weather forecasts for the following month, which are obtained in advance by meteorological departments. Crop data includes planted area, crop rotation time, type, and underlying surface data.

[0090] Module M2: Constructs the watershed SWAT model, using meteorological and crop data as inputs. It runs monthly to calculate the runoff of each river and the concentration and output of non-point source pollutants (NPPs). NPPs include total phosphorus and total nitrogen. The watershed SWAT model construction includes: Module M2.1: Obtaining basic watershed data. This includes topographic data, land use data, soil data, precipitation data, and evaporation data. Module M2.2: Model construction. This includes sub-watershed delineation, hydrological response unit delineation, and model sensitivity analysis. The model includes a hydrological module and a water quality module. Module M2.3: Calibrates the parameters of the hydrological module using measured runoff data and the parameters of the water quality module using measured river water quality data, thus completing parameter confirmation.

[0091] Module M3: Constructs a MIKE21 model of a lake including the estuary region. Module M3 includes: Module M3.1: Acquiring basic lake data. Basic lake data includes underwater topographic data, inflow river water quality data, water quantity data, precipitation data, evaporation data, and wind field data. Module M3.2: Model construction. The model construction modules include mesh generation, topographic interpolation, boundary condition setting, and initial condition setting. The model includes a hydrodynamic module and a water quality module. Module M3.3: Parameter determination. The parameters of the hydrodynamic module are calibrated using measured lake water level data, and the parameters of the water quality module are calibrated using measured water quality data from lake sampling points.

[0092] Module M4: This module uses the output of non-point source pollutants from each river as the boundary condition input for the lake's MIKE21 model. It simulates the changes in non-point source pollutants in the lake for the current month and predicts the changes for the following month, obtaining lake water quality indicators. These indicators include the concentration of non-point source pollutants.

[0093] Module M5: Determines whether the current lake meets the requirements of the lake's water function zone based on the lake's water quality indicators. If yes, it triggers module M6. If no, it gradually reduces the input load of each inflow river sub-basin by a preset step size, triggering module M4.

[0094] Module M6: Lists the cumulative minimum inflow load scenarios and identifies the risk zones under these scenarios. Risk zones refer to the hydrological response units of the sub-basins and maximum discharge loads under these scenarios.

[0095] Module M7: Call the watershed SWAT model again to set up different scenarios for watershed agricultural non-point source pollution control.

[0096] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0097] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for identifying the risk of agricultural non-point source pollution in a lake basin, characterized in that, The method comprises the following steps: Step S1: obtaining meteorological data and crop data; Step S2: constructing a watershed SWAT model, inputting the meteorological data and crop data into the watershed SWAT model, running monthly, calculating the runoff of each river and the concentration and output of each river non-point source pollutant; the construction of the model comprises sub-basin division, hydrological response unit division and model sensitivity analysis; Step S3: constructing a lake MIKE21 model containing an estuary region; Step S4: inputting the output of each river non-point source pollutant as the boundary condition of the lake MIKE21 model, simulating the current month and predicting the next month lake non-point source pollutant change, obtaining the lake water quality index; Step S5: determining whether the current lake meets the requirements of the lake water function area according to the lake water quality index, if yes, executing Step S6; if not, reducing the input load of each sub-basin of the river entering the lake by a preset step length, and executing Step S4; Step S6: listing the cumulative minimum reduction of the load entering the lake scenario, and determining the risk area under the scenario; the risk area refers to the sub-basin and the hydrological response unit of the maximum discharge load under the scenario; Step S7: calling the watershed SWAT model again, and setting different scenarios for the prevention and control of watershed agricultural non-point source pollution. 2.The method for identifying agricultural non-point source pollution risk suitable for lake basin according to claim 1, characterized in that, The meteorological data comprises weather forecast data of the next month, which is obtained in advance by a meteorological department; The crop data comprises the planting area, rotation time, type and underlying surface data of the planted crops; The non-point source pollutant comprises total phosphorus and total nitrogen; The lake water quality index comprises the concentration of the non-point source pollutant. 3.The method for identifying agricultural non-point source pollution risk suitable for lake basin according to claim 1, characterized in that, The construction of the watershed SWAT model comprises the following steps: Step S2.1: obtaining watershed basic data; Step S2.2: model construction, the model comprising a hydrological module and a water quality module; Step S2.3: calibrating the hydrological module parameters through measured runoff data, and calibrating the water quality module parameters through measured river water quality data, thereby completing parameter confirmation. 4.The method for identifying agricultural non-point source pollution risk suitable for lake basin according to claim 1, characterized in that, The step S3 comprises the following steps: Step S3.1: obtaining lake basic data; Step S3.2: model construction, the model construction comprising grid division, terrain interpolation, boundary condition setting and initial condition setting, the model comprising a hydrodynamic module and a water quality module; Step S3.3: parameter determination, calibrating the hydrodynamic module parameters through measured lake water level data, and calibrating the water quality module parameters through measured water quality data of the lake sampling points.

5. The agricultural non-point source pollution risk identification method suitable for a lake basin according to claim 4, characterized in that, The watershed basic data comprises watershed terrain data, land use data, soil data, precipitation data and evaporation data; The lake basic data comprises lake underwater terrain data, river water quality data entering the lake, water quantity data, precipitation data, evaporation data and wind field data.

6. A system for identifying agricultural non-point source pollution risk applicable to a lake basin, characterized in that, The method comprises the following steps: Module M1: obtaining meteorological data and crop data; Module M2: constructing a watershed SWAT model, inputting the meteorological data and crop data into the watershed SWAT model, running monthly, calculating the runoff of each river and the concentration and output of each river non-point source pollutant; the construction of the model comprises sub-basin division, hydrological response unit division and model sensitivity analysis; Module M3: constructing a lake MIKE21 model containing estuary areas; Module M4: inputting the output of each river surface source pollutant as a boundary condition of the lake MIKE21 model, simulating the current month and predicting the next month to obtain a lake water quality index; Module M5: determining whether the current lake meets the requirements of the lake water function area according to the lake water quality index, if yes, triggering module M6; if not, reducing the input load of each sub-basin of the river entering the lake by a preset step, triggering module M4; Module M6: listing the cumulative minimum reduction of the lake load scenario, and determining the risk area under the scenario; the risk area refers to the sub-basin and the hydrological response unit of the maximum discharge load under the scenario; Module M7: calling the basin SWAT model again, setting different scenarios for basin agricultural non-point source pollution prevention and control.

7. The agricultural non-point source pollution risk identification system suitable for a lake basin according to claim 6, characterized in that, The weather forecast data is obtained in advance by the meteorological department; The crop data includes the planting area, rotation time, type and underlying surface data of the planted crops; The non-point source pollutants include total phosphorus and total nitrogen; The lake water quality index includes the concentration of non-point source pollutants.

8. The agricultural non-point source pollution risk identification system suitable for lake basin of claim 6, wherein, The construction of the basin SWAT model includes: Module M2.1: obtaining basin basic data; Module M2.2: model construction, the model includes a hydrological module and a water quality module; Module M2.3: calibrating the hydrological module parameters through measured runoff data, and calibrating the water quality module parameters through measured river water quality data, and then completing parameter confirmation.

9. The agricultural non-point source pollution risk identification system suitable for lake basin according to claim 6, characterized in that, The module M3 includes: Module M3.1: obtaining lake basic data; Module M3.2: model construction, the model construction module includes grid division, terrain interpolation, boundary condition setting and initial condition setting, and the model includes a hydrodynamic module and a water quality module; Module M3.3: parameter determination, calibrating the hydrodynamic module parameters through measured lake water level data, and calibrating the water quality module parameters through measured water quality data at lake sampling points.

10. The agricultural non-point source pollution risk identification system suitable for a lake basin according to claim 9, characterized in that, The basin basic data includes basin terrain data, land use data, soil data, precipitation data and evaporation data; The lake basic data includes lake underwater terrain data, river water quality data entering the lake, water quantity data, precipitation data, evaporation data and wind field data.

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