A simulation calculation method for agricultural non-point source load

Abstract

The present invention provides a method for simulating and calculating agricultural non-point source loads, which relates to the field of agricultural non-point source pollution calculation. The method comprises: constructing an agricultural non-point source load calculation model, reading surface runoff time series data of each hydrological response unit in a watershed, and setting corresponding model parameters; setting a time step, and using the surface runoff time series data and the model parameters, sequentially calculating the soil thickness that can be washed within the current time step, the amount of pollutants that can be washed per unit area, and the amount of pollutants actually washed per unit area, ultimately obtaining an updated amount of soil pollutant concentration for the current time step; performing the above calculations for all hydrological response units within a simulation period, obtaining a time series of the updated amount of soil pollutant concentration, and calculating the agricultural non-point source load of the watershed within the simulation period in combination with the surface runoff time series data. The present invention incorporates the natural processes of agricultural non-point source washout into the model, resulting in a more accurate and interpretable agricultural non-point source load.

Description

Technical Field

[0001] The present invention relates to the field of agricultural non-point source pollution calculation, and in particular to a method for simulating and calculating agricultural non-point source load. Background Art

[0002] Agricultural non-point source pollution refers to the pollution of the ecological environment caused by nitrogen, phosphorus, organic matter and other nutrients produced in the agricultural production process due to the irrational use of chemical inputs such as fertilizers, pesticides, and ground film, as well as the untimely or improper treatment of livestock and aquaculture waste, crop straw, etc., which, driven by rainfall and topography, use surface and underground runoff and soil erosion as carriers, accumulate excessively in the soil or enter the receiving water bodies.

[0003] Agricultural non-point source pollution is one of the main sources of water pollution in river basins. Simulating agricultural non-point source surface washout processes, using existing technologies, can better understand and predict the impact of agricultural activities on water resource utilization and water quality. This can provide decision support for optimizing water resource management and protection, and provide a scientific basis for water environment governance and protection.

[0004] Currently, methods for simulating agricultural non-point source surface washoff primarily include empirical models, physical models, remote sensing technology, and numerical simulation. Empirical models are simple and easy to use, but based on historical data, they have low prediction accuracy and cannot predict future changes. Physical models can simulate the physical processes of surface runoff, soil erosion, and pollutant transport, offering high prediction accuracy, but the models are complex and expensive to establish and maintain. Remote sensing technology offers high spatial resolution and rapid data acquisition, but the data processing process is complex and requires specialized technology and equipment. Numerical simulation, currently the most widely used method, simulates agricultural non-point source surface washoff and pollutant transport processes by establishing numerical models across multiple disciplines. It offers high prediction accuracy and flexibility, and can comprehensively consider multiple influencing factors, such as rainfall, soil type, topography, and agricultural management practices.

[0005] However, most current watershed numerical models have significant shortcomings when simulating agricultural non-point source loads. These models often adopt simplified approaches, focusing solely on the flushing effect of rainfall runoff as the core driver of pollutant migration. They ignore the complex influence of soil properties and land use types on the spatial distribution of pollutants. For example, they fail to consider crop root uptake and microbial degradation in agricultural areas, and make idealized assumptions about fertilization processes. Summary of the Invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a method for simulating and calculating agricultural non-point source loads by superimposing potential scour thickness and exponential scour parameters to improve the accuracy and interpretability of the model.

[0007] Technical solution: A method for simulating and calculating agricultural non-point source loads, comprising the following steps:

[0008] S1. Construct an agricultural nonpoint source load calculation model, read the surface runoff time series data of each hydrological response unit in each subcatchment within the target watershed; set the model parameters corresponding to each agricultural-related hydrological response unit in each subcatchment group, including initial soil pollutant concentration, amount of new pollutants, soil thickness, first-order degradation coefficient of pollutants, background concentration of soil pollutants, potential runoff depth that can wash away 50% of pollutants, surface runoff required to wash away 90% of accumulated surface pollutants, and adjustment coefficient;

[0009] S2. Select a simulation period, read the surface runoff of the first time step in the simulation period from the surface runoff time series data of a hydrological response unit related to agriculture in a sub-catchment, and calculate the soil thickness that can be washed away in the hydrological response unit in the current time step using the soil thickness of the hydrological response unit grouped in the sub-catchment, the runoff depth that can potentially wash away 50% of the pollutants, and the adjustment factor;

[0010] S3. Calculate the amount of pollutants that can be washed per unit area of ​​the hydrological response unit within the current time step using the washable soil thickness, the corresponding initial soil pollutant concentration, and the soil pollutant background concentration;

[0011] S4. Calculate the actual amount of pollutants flushed per unit area of ​​the hydrological response unit within the current time step using the amount of pollutants that can be flushed per unit area and the corresponding surface runoff required to flush away 90% of the accumulated surface pollutants.

[0012] S5. Calculate the first-order degradation amount of soil pollutant concentration using the first-order degradation coefficient of pollutants and the initial soil pollutant concentration. Then, calculate the updated soil pollutant concentration for the hydrological response unit in the sub-catchment within the current time step using the newly added pollutant amount, the actual amount of pollutants washed off per unit area, the first-order degradation amount of soil pollutant concentration, and soil thickness. Use the updated soil pollutant concentration for the current time step as the initial soil pollutant concentration for the next time step.

[0013] S6. Repeat steps S2 to S5, perform calculations for all sub-catchments and all hydrological response units respectively, record the time series of soil pollutant concentration updates and actual pollutant washout during the simulation period until the end of the simulation period, and use the time series of soil pollutant concentration updates and actual pollutant washout during the simulation period for all sub-catchments and all agriculture-related hydrological response units and the corresponding surface runoff time series data to calculate the agricultural non-point source load of the target basin during the simulation period.

[0014] Specifically, in step S2, the calculation formula for the thickness of soil that can be washed away is:

[0015] ,

[0016] Where: is the thickness of soil that can be washed away, is the soil thickness, is surface runoff, is the simulation coefficient, is the runoff depth that can potentially wash away 50% of the pollutants, Represents the current time step.

[0017] Specifically, in step S3, the calculation formula for the amount of pollutants that can be washed per unit area is:

[0018] ,

[0019] Where: is the amount of pollutants that can be washed per unit area, is the updated amount of soil pollutant concentration in the previous time step, and the first time step is the initial soil pollutant concentration, is the background concentration of soil pollutants.

[0020] Specifically, in step S4, the calculation formula for the actual amount of pollutants flushed per unit area is:

[0021] ,

[0022] Where: is the actual amount of pollutants washed per unit area, The amount of surface runoff required to flush away 90% of accumulated pollutants on the surface.

[0023] Specifically, in step S5, the calculation formula for the first-order degradation amount of soil pollutant concentration is:

[0024] ,

[0025] Where: is the first-order degradation amount of soil pollutant concentration, is the first-order degradation coefficient of the pollutant.

[0026] Specifically, in step S5, the calculation formula for the updated amount of soil pollutant concentration is:

[0027] ,

[0028] Where: is the updated concentration of soil pollutants, The amount of new pollutants.

[0029] Specifically, in step S6, the agricultural non-point source load includes the agricultural non-point source surface erosion load and the agricultural non-point source surface storage load. The agricultural non-point source surface erosion load is calculated as follows:

[0030] ,

[0031] Where: is the surface wash load from agricultural non-point sources, is the area of ​​the hydrological response unit within the subcatchment;

[0032] The calculation formula for agricultural non-point source surface storage load is:

[0033] ,

[0034] Where: Surface storage load for agricultural nonpoint sources;

[0035] The agricultural non-point source loads of each sub-catchment are accumulated to obtain the non-point source loads of each agricultural-related hydrological response unit in the target basin during the simulation period.

[0036] Specifically, in step S1, the surface runoff time series data of different hydrological response units in the basin are obtained by the following steps:

[0037] Obtain elevation DEM data, land use type data, soil data and historical meteorological data of the target watershed, and construct a watershed hydrological and water quality model. Use the watershed hydrological and water quality model to calculate and output the surface runoff time series data of different hydrological response units in each sub-catchment area in the watershed.

[0038] Specifically, soil data includes soil type and soil physical and chemical properties; elevation DEM data is used to extract watershed range and calculate terrain characteristics; historical meteorological data includes rainfall, temperature, humidity, wind direction, wind speed, air pressure, evaporation, cloud cover, sunshine and solar radiation.

[0039] Specifically, the steps for constructing a watershed hydrological and water quality model include: selecting a watershed hydrological and water quality simulation computing platform that matches the target watershed, dividing the sub-catchment area according to the elevation DEM data; converting the elevation DEM data into slope space vector data, integrating the land use type data, soil data and slope space vector data, and constructing a hydrological response unit; simulating macro-hydrological processes and macro-water quality processes, where simulating macro-hydrological processes refers to simulating the hydrological cycle of a small watershed, including simulating rainfall interception, infiltration, evaporation and transpiration, surface runoff, and regional runoff and confluence processes; simulating macro-water quality processes includes simulating pollutant accumulation, scouring, degradation and transport processes; and verifying the preliminary operation results of the watershed hydrological and water quality model based on the regional runoff coefficient and annual average pollutant concentration survey data.

[0040] Beneficial effects: Compared with the prior art, the present invention has the following significant effects:

[0041] 1. Refined expression of soil pollutant background concentration: This invention uses hydrological response units as the smallest division unit for different types of land in the target watershed and adopts differentiated expression of soil pollutant background concentration. It can predict the pollutant washoff concentration caused by rainfall in the absence of additional pollution sources, more realistically reflect the impact of existing pollutants on different types of land, and thus improve the simulation accuracy of pollutant concentration in runoff.

[0042] 2. Dynamically simulate changes in soil pollutant concentrations during fertilization: Taking into account different soil types, crop varieties, and fertilization methods, pollutants are applied to the soil at different depths. During the flushing process, the amount of soil flushed is dynamically calculated based on the precipitation runoff. This can more accurately simulate the interaction between precipitation and pollutant flushing.

[0043] 3. Dynamic simulation of changes in soil pollutant concentration degradation process: According to different fertilizer types and soil characteristics, the degradation process of pollutants in the soil under the action of microbial activity and crop absorption is simulated, making the pollutant removal process more consistent with the natural attenuation process in the ecosystem, greatly improving the authenticity and accuracy of the model's simulation of the pollutant removal process.

[0044] 4. Dynamic simulation of changes in soil pollutant concentration during the washout process: This simulation simulates how the amount of pollutants in the soil changes with runoff depth, targeting different soil properties and runoff depths. This allows simulation of fertilizer application to move beyond surface application and instead allows fertilizer to be applied to different depths of soil based on crop root needs and soil adsorption characteristics. This approach more accurately reflects the changes in pollutant concentration at different depths as fertilization depth changes, allowing for more accurate simulation of pollutant washout at different depths during the washout process. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of the method of the present invention.

[0046] Figure 2 This is a time series diagram of surface runoff in the watershed of Example 1 of the present invention.

[0047] Figure 3 This is a time series comparison chart of agricultural non-point source surface erosion loads in the watershed of Example 1 of the present invention.

[0048] Figure 4 This is a time series comparison chart of surface storage load of agricultural non-point sources in the watershed of Example 1 of the present invention. DETAILED DESCRIPTION

[0049] A preferred embodiment of the present invention is further described below with reference to the accompanying drawings.

[0050] See also Figure 1 As shown, the present invention provides a method for simulating and calculating agricultural non-point source load, comprising the following steps:

[0051] S1. To establish the computational model, we first need to obtain modeling data for the target watershed, including elevation DEM data, land use type data, soil data, and historical meteorological data. Soil data includes soil type and soil physical and chemical properties. Elevation DEM data is used to extract the watershed extent and calculate terrain characteristics. Land use type data refers to land resource units with the same land use pattern. It is divided according to regional differences in land use and is a basic regional unit that reflects the use, nature, and distribution of land, such as paddy fields, dry land, orchards, rural areas, towns, water bodies, wetlands, and roads. Historical meteorological data includes rainfall, temperature, humidity, wind direction, wind speed, air pressure, evaporation, cloud cover, sunshine, and solar radiation. These modeling data are spatially superimposed, unified into a unified coordinate system, and formatted. Based on this data, a comprehensive modeling database for the target watershed is constructed.

[0052] After completing the modeling data processing, construct a watershed hydrological and water quality model, select a watershed hydrological and water quality simulation computing platform (such as SWAT, HSPF, LSPC, MIKE SHE) that matches the target watershed, and divide the sub-catchment area according to the elevation DEM data; convert the elevation DEM data into slope space vector data, and integrate the land use type data, soil data and slope space vector data to construct a hydrological response unit; simulate macro-hydrological processes and macro-water quality processes, among which simulating macro-hydrological processes refers to simulating the hydrological cycle of a small watershed, including simulating rainfall interception, infiltration, evaporation and transpiration, surface runoff, and regional runoff and confluence processes; simulating macro-water quality processes includes simulating pollutant accumulation, scouring, degradation and transport processes; based on the regional runoff coefficient and annual average pollutant concentration survey data, verify the preliminary operation results of the watershed hydrological and water quality model to ensure the rationality of the model simulation.

[0053] The watershed hydrological and water quality model is used to calculate and output the surface runoff time series data of different agricultural-related hydrological response units in each sub-catchment area in the watershed. In this embodiment, the time step is 1 hour.

[0054] Existing watershed hydrological and water quality models do not fully consider some key natural processes when simulating agricultural non-point source loads:

[0055] (1) Lack of crop root absorption and microbial degradation: In agricultural areas, the active absorption of nutrients by crop roots and the decomposition of pollutants by soil microorganisms are the core mechanisms for the natural reduction of pollutant concentrations. However, existing models only focus on the migration of pollutants caused by runoff erosion, and do not include the nutrient absorption process and microbial metabolism during the crop growth cycle into the calculation framework. As a result, the simulation results cannot truly reflect the dynamic cycle of pollutants in the soil-plant-atmosphere system.

[0056] (2) Idealized assumptions about the fertilization process: In agricultural fertilization simulations, the models generally have three major idealized defects:

[0057] The single treatment of surface fertilization: All fertilizers are mechanically applied to the soil surface, ignoring the impact of diversified fertilization methods such as deep application and side application on the vertical distribution of pollutants in actual production.

[0058] Complete assumption of pollutant migration: It assumes that surface pollutants can be 100% washed away by rainfall runoff, without considering the adsorption and interception of pollutants by different depths of soil.

[0059] Staticization of soil vertical profiles: Pollutant concentrations are considered to exist only in the surface layer, without reflecting the accumulation and migration of pollutants in deep soil layers. This is contrary to the actual law that pollutants infiltrate into different soil layers with water.

[0060] (3) Simplification of key driving factors: The model does not consider the following key influencing factors in the simulation of the fertilization process:

[0061] Differences in fertilization depth: Different fertilization methods (such as deep application of base fertilizer and shallow application of topdressing) lead to different initial distributions of pollutants in the soil, which in turn affects their migration path and loss risk.

[0062] Soil type heterogeneity: Different soil textures, such as clay and sand, have significant differences in their adsorption capacity for pollutants, but this is not taken into account in the model.

[0063] Crop type and growth stage: Different crops have different nutrient requirements and root distribution characteristics, which determine the depth of fertilization and the bioavailability of pollutants, but the model does not reflect this dynamic process.

[0064] To solve the above problems, the present invention improves the original agricultural non-point source load module in the basin hydrological and water quality model and adds a series of new model parameters:

[0065] (1) Adding differential expression of pollutant background concentration;

[0066] (2) first-order degradation expression of pollutant concentration in added soil;

[0067] (3) Add the dynamic expression of the amount of pollutants in the soil at different runoff depths;

[0068] (4) Add the expression of pollutant concentration changes at different depths in the soil.

[0069] Specifically, the present invention constructs an improved agricultural non-point source load calculation model, reads the surface runoff time series data of different agricultural-related hydrological response units in each sub-catchment area in the target watershed; sets the model parameters corresponding to the different agricultural-related hydrological response units in each group of sub-catchments, including the initial soil pollutant concentration (mg / L), the amount of new pollutants (lb / acre / day), the soil thickness (m), the first-order degradation coefficient of pollutants ( / day), the background concentration of soil pollutants (mg / L), the potential runoff depth that can wash away 50% of pollutants (inches), the surface runoff required to wash away 90% of the accumulated surface pollutants (inches / hour), and the adjustment coefficient.

[0070] S2. Select a simulation period and read the surface runoff of the first time step in the simulation period from the surface runoff time series data of a certain agricultural-related hydrological response unit in a certain subcatchment, that is, the surface runoff within the first hour of the first day. Use the corresponding soil thickness, the potential runoff depth that can wash away 50% of the pollutants, and the adjustment factor to calculate the soil thickness that can be washed away in the current time step for this hydrological response unit:

[0071] ,

[0072] Where: is the thickness of soil that can be washed away in the current time step, in m / hour. is the soil thickness, is the surface runoff in the current time step, is the simulation coefficient, is the runoff depth that can potentially wash away 50% of the pollutants, Represents the current time step.

[0073] S3. Calculate the amount of pollutants that can be washed per unit area of ​​the hydrological response unit within the current time step using the washable soil thickness, the corresponding initial soil pollutant concentration, and the soil pollutant background concentration:

[0074] ,

[0075] Where: is the amount of pollutants that can be washed per unit area, in lb / acre / hour. is the updated amount of soil pollutant concentration in the previous time step, and the first time step is the initial soil pollutant concentration, is the background concentration of soil pollutants, is the unit conversion factor.

[0076] S4. Using the amount of pollutants that can be washed per unit area and the corresponding surface runoff required to wash away 90% of the accumulated surface pollutants, calculate the actual amount of pollutants washed per unit area of ​​the hydrological response unit in the current time step:

[0077] ,

[0078] Where: is the actual amount of pollutants washed per unit area, The amount of surface runoff required to flush away 90% of accumulated pollutants on the surface.

[0079] S5. Use the first-order degradation coefficient of pollutants and the initial soil pollutant concentration to calculate the first-order degradation amount of soil pollutant concentration. Then use the amount of new pollutants, the amount of pollutants actually washed away per unit area, the first-order degradation amount of soil pollutant concentration and soil thickness to calculate the updated amount of soil pollutant concentration of the hydrological response unit within the current time step. The updated amount of soil pollutant concentration in the current time step is used as the initial soil pollutant concentration in the next time step.

[0080] The calculation formula for the first-order degradation of soil pollutant concentration is:

[0081] ,

[0082] Where: is the first-order degradation amount of soil pollutant concentration, is the first-order degradation coefficient of the pollutant.

[0083] The calculation formula for the updated amount of soil pollutant concentration is:

[0084] ,

[0085] Where: is the updated concentration of soil pollutants, For the amount of new pollutants, is the unit conversion factor.

[0086] S6. Repeat steps S2 to S5 to calculate all hydrological response units and record the updated soil pollutant concentration during the simulation period. The agricultural non-point source load of the target basin during the simulation period is calculated by using the time series of soil pollutant concentration updates and the corresponding surface runoff time series data of all hydrological response units during the simulation period until the end of the simulation period.

[0087] Agricultural non-point source load includes agricultural non-point source surface wash load and agricultural non-point source surface storage load. The calculation formula for agricultural non-point source surface wash load is:

[0088] ,

[0089] Where: is the surface wash load from agricultural non-point sources, is the area of ​​the hydrological response unit;

[0090] The calculation formula for agricultural non-point source surface storage load is:

[0091] ,

[0092] Where: The surface storage load of agricultural non-point sources is is the unit conversion factor;

[0093] The agricultural non-point source loads of each sub-catchment in the target basin are accumulated to obtain the non-point source loads of each agricultural-related hydrological response unit in the target basin during the simulation period.

[0094] Example 1

[0095] In this embodiment, the statistical data of a river basin in 2023 is substituted into the above calculation method for statistics and calculation. Figure 2 This is a time series diagram of surface runoff in a sub-catchment area within the basin. To demonstrate the effectiveness of this solution, the original agricultural non-point source load module was used to calculate statistical data, and the results were compared with those obtained using the improved agricultural non-point source load module of the present invention. Figure 3 This is a time series diagram of the surface wash load from agricultural non-point sources in a sub-catchment area of ​​the basin. Figure 3 The yellow line in the middle represents the time series of agricultural non-point source surface wash load calculated using the original agricultural non-point source load module. Figure 3 The middle blue line represents the time series of agricultural non-point source surface scour loads calculated using the improved agricultural non-point source load module of the present invention. It can be seen intuitively that throughout the year, the agricultural non-point source surface scour load data obtained using the calculation method provided by the present invention are significantly lower than those obtained using the traditional calculation method that only considers the scour effect of surface runoff. This result is consistent with the trend of the theoretical analysis results above and is more in line with objective laws. Figure 4 It is a time series diagram of surface storage load of agricultural non-point sources in a sub-catchment area of ​​the basin; Figure 4 The yellow curve in the middle represents the time series of agricultural non-point source surface storage load calculated using the original agricultural non-point source load module. Figure 4The middle blue curve represents the time series of the agricultural non-point source surface storage load calculated by adding the improved agricultural non-point source load module of the present invention. It can be seen intuitively that throughout the year, the agricultural non-point source surface storage load data obtained by the calculation method provided by the present invention are higher than the agricultural non-point source surface storage load data obtained by the traditional calculation method that only considers the scouring effect of surface runoff. This result is also consistent with the trend of the theoretical analysis results above and is more in line with objective laws.

Claims

1. A method for simulating and calculating agricultural non-point source loads, characterized in that: The following steps are involved: S1. Build an agricultural non-point source load calculation model and read the surface runoff time series data of each hydrological response unit in the target watershed; Set the model parameters corresponding to each hydrological response unit; S2. Select a simulation period, read the surface runoff of the first time step in the simulation period from the surface runoff time series data of a hydrological response unit, and calculate the soil thickness that can be washed away in the current time step of the hydrological response unit using the corresponding model parameters; S3, using the flushable soil thickness and corresponding model parameters to calculate the amount of pollutants that can be flushed per unit area of ​​the hydrological response unit in the current time step; S4. Calculate the actual amount of pollutants flushed per unit area of ​​the hydrological response unit within the current time step using the amount of pollutants that can be flushed per unit area and the corresponding model parameters; S5. Calculate the first-order degradation of soil pollutant concentration using the model parameters, then calculate the updated soil pollutant concentration for the hydrological response unit within the current time step using the actual amount of pollutants flushed per unit area, the first-order degradation of soil pollutant concentration, and the model parameters. Use the updated soil pollutant concentration for the current time step as the initial soil pollutant concentration for the next time step. S6. Repeat steps S2 to S5, perform calculations for all hydrological response units respectively, record the time series data of the updated amount of soil pollutant concentration and the actual amount of pollutants washed away during the simulation period, until the end of the simulation period, and use the time series data of the updated amount of soil pollutant concentration and the actual amount of pollutants washed away during the simulation period of all hydrological response units and the corresponding time series data of surface runoff to calculate the agricultural non-point source load of the target basin during the simulation period; the agricultural non-point source load includes the agricultural non-point source surface wash load and the agricultural non-point source surface storage load. The calculation formula of the agricultural non-point source surface wash load is: ， Where: is the surface wash load from agricultural non-point sources, is the actual amount of pollutants washed per unit area, is the area of ​​the hydrological response unit; The calculation formula for agricultural non-point source surface storage load is: ， Where: The surface storage load of agricultural non-point sources is is the updated concentration of soil pollutants, is the soil thickness; Finally, the agricultural non-point source load of each hydrological response unit in the target basin during the simulation period is obtained.

2. The method for simulating and calculating agricultural non-point source load according to claim 1, wherein: In step S1, the model parameters include the initial soil pollutant concentration, the amount of newly added pollutants, soil thickness, the first-order degradation coefficient of pollutants, the background concentration of soil pollutants, the runoff depth that can potentially wash away 50% of the pollutants, the surface runoff volume required to wash away 90% of the accumulated surface pollutants, and the adjustment coefficient.

3. The method for simulating and calculating agricultural non-point source load according to claim 2, wherein: In step S2, the calculation formula for the thickness of soil that can be washed away is: ， Where: is the thickness of soil that can be washed away, is the soil thickness, is surface runoff, is the simulation coefficient, is the runoff depth that can potentially wash away 50% of the pollutants, Represents the current time step.

4. The method for simulating and calculating agricultural non-point source load according to claim 3, characterized in that: In step S3, the calculation formula for the amount of pollutants that can be washed per unit area is: ， Where: is the amount of pollutants that can be washed per unit area, is the updated amount of soil pollutant concentration in the previous time step, and the first time step is the initial soil pollutant concentration, is the background concentration of soil pollutants.

5. The method for simulating and calculating agricultural non-point source load according to claim 4, characterized in that: In step S4, the calculation formula for the actual amount of pollutants flushed per unit area is: ， Where: is the actual amount of pollutants washed per unit area, The amount of surface runoff required to flush away 90% of accumulated pollutants on the surface.

6. The method for simulating and calculating agricultural non-point source load according to claim 5, characterized in that: In step S5, the calculation formula for the first-order degradation amount of soil pollutant concentration is: ， Where: is the first-order degradation amount of soil pollutant concentration, is the first-order degradation coefficient of the pollutant.

7. The method for simulating and calculating agricultural non-point source load according to claim 6, characterized in that: In step S5, the calculation formula for the updated amount of soil pollutant concentration is: ， Where: is the updated concentration of soil pollutants, The amount of new pollutants.

8. The method for simulating and calculating agricultural non-point source load according to claim 1, wherein: In step S1, the surface runoff time series data of different hydrological response units in the basin are obtained by the following steps: Obtain elevation DEM data, land use type data, soil data and historical meteorological data of the target watershed, and build a watershed hydrological and water quality model. Use the watershed hydrological and water quality model to calculate and output the surface runoff time series data of different hydrological response units in the watershed.

9. The method for simulating and calculating agricultural non-point source load according to claim 8, characterized in that: The soil data includes soil type and soil physical and chemical properties; the elevation DEM data is used to extract the watershed range and calculate the terrain characteristics; the historical meteorological data includes rainfall, temperature, humidity, wind direction, wind speed, air pressure, evaporation, cloud cover, sunshine and solar radiation; The steps of constructing the watershed hydrological and water quality model include: selecting a watershed hydrological and water quality simulation computing platform that matches the target watershed, converting elevation DEM data into slope spatial vector data, fusing land use type data, soil data and slope spatial vector data, and constructing a hydrological response unit; simulating macro-hydrological processes and macro-water quality processes, wherein simulating macro-hydrological processes refers to simulating the hydrological cycle of a small watershed, including simulating rainfall interception, infiltration, evaporation and transpiration, surface runoff, and regional runoff and confluence processes; simulating macro-water quality processes includes simulating pollutant accumulation, scouring, degradation and transport processes; and verifying the preliminary operating results of the watershed hydrological and water quality model based on regional runoff coefficients and annual average pollutant concentration survey data.

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