Agricultural non-point source load simulation calculation method

Abstract

The invention provides an agricultural non-point source load simulation calculation method, and relates to the field of agricultural non-point source pollution calculation, and the method comprises the steps: constructing an agricultural non-point source load calculation model, reading surface runoff time sequence data of each hydrological response unit in a drainage basin, and setting corresponding model parameters; setting a time step length, sequentially calculating the thickness of soil capable of being scoured in the current time step length, the amount of pollutants capable of being scoured in unit area and the amount of pollutants actually scoured in unit area by utilizing surface runoff time sequence data and model parameters, and finally obtaining the updating amount of the concentration of the soil pollutants in the current time step length. All hydrological response units are respectively subjected to the above calculation in the simulation time period to obtain a time sequence of the soil pollutant concentration updating amount, and the agricultural non-point source load of the drainage basin in the simulation time period is calculated in combination with the surface runoff time sequence data. According to the method, the natural process in the agricultural non-point source scouring process is fused into the model, and the obtained agricultural non-point source load is more accurate and more interpretable.

Description

Technical Field

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

[0002] Agricultural non-point source pollution refers to the nitrogen, phosphorus, organic matter and other nutrients generated during agricultural production due to the unreasonable use of chemical inputs such as chemical fertilizers, pesticides, and plastic films, as well as the untimely or improper treatment of livestock and poultry aquaculture waste, crop straw, etc. Driven by rainfall and terrain together, carried by surface runoff, subsurface runoff and soil erosion, it accumulates excessively in the soil or enters the receiving water body, causing pollution to the ecological environment.

[0003] Agricultural non-point source pollution is one of the main sources of water pollution in river basins. In the prior art, simulating the process of agricultural non-point source surface scouring can better understand and predict the utilization and impact of agricultural activities on water resources and the impact on water quality, provide decision-making support for optimizing water resource management and protection, and provide a scientific basis for water environment governance and protection.

[0004] Currently, the methods for simulating agricultural non-point source surface scouring mainly include empirical model method, physical model method, remote sensing technology method and numerical simulation method. The empirical model method is simple and easy to use, but based on past empirical data, the prediction accuracy is low and it cannot predict new changes that may occur in the future; the physical model method can simulate the physical processes of surface runoff, soil erosion and pollutant transport, with high prediction accuracy, but the model is complex and the establishment and maintenance costs are high; the remote sensing technology method has high spatial resolution and can quickly obtain data, but the data processing process is complex and requires professional technology and equipment support; the numerical simulation method is the most widely used method currently. By establishing numerical models of multiple disciplines to simulate the process of agricultural non-point source surface scouring and pollutant transport, it has high prediction accuracy and flexibility, and can comprehensively consider various influencing factors, such as rainfall, soil type, terrain, agricultural management measures, etc.

[0005] However, there are obvious deficiencies in current numerical models for most river basins when simulating agricultural non-point source load. These models usually adopt a simplified processing method, only taking the scouring effect of rainfall runoff as the core driving factor for pollutant migration, but ignoring the complex influence of soil characteristics and land use types on the spatial distribution of pollutants, such as not considering the absorption of crop roots and the degradation of microorganisms in agricultural areas, and making idealized assumptions about the fertilization process. Summary of the Invention

[0006] Object of the Invention: The object of the present invention is to provide a method for simulating and calculating agricultural non-point source load that superimposes the potential scouring thickness and the exponential scouring parameter to improve the accuracy and interpretability of the model.

[0007] Technical solution: An agricultural non-point source load simulation calculation method, comprising the following steps: S1. Construct an agricultural non-point source load calculation model, and read the surface runoff time series data of each hydrological response unit in each sub-watershed within the target watershed; set the model parameters corresponding to each agricultural-related hydrological response unit in each group of sub-watersheds, including the initial soil pollutant concentration, the amount of newly added pollutants, the soil thickness, the first-order degradation coefficient of pollutants, the background concentration of soil pollutants, the runoff depth of potentially scourable 50% of pollutants, the surface runoff volume required to wash away 90% of the surface accumulated pollutants, and the adjustment coefficient; S2. Select a simulation period, and read the surface runoff at the first time step within the simulation period from the surface runoff time series data of a certain agricultural-related hydrological response unit in a certain sub-watershed, and calculate the scourable soil thickness of this hydrological response unit within the current time step by using the soil thickness, the runoff depth of potentially scourable 50% of pollutants, and the adjustment coefficient of this hydrological response unit grouped by this sub-watershed; S3. Calculate the amount of pollutants scourable per unit area of this hydrological response unit within the current time step by using the scourable soil thickness and the corresponding initial soil pollutant concentration and background concentration of soil pollutants; S4. Calculate the actual amount of pollutants scoured per unit area of this hydrological response unit within the current time step by using the amount of pollutants scourable per unit area and the corresponding surface runoff volume required to wash away 90% of the surface accumulated pollutants; S5. Calculate the first-order degradation amount of soil pollutant concentration by using the first-order degradation coefficient of pollutants and the initial soil pollutant concentration, and then calculate the updated amount of soil pollutant concentration of this hydrological response unit within this sub-watershed within the current time step by using the amount of newly added pollutants, the actual amount of pollutants scoured per unit area, the first-order degradation amount of soil pollutant concentration, and the soil thickness, and use the updated amount of soil pollutant concentration at the current time step as the initial soil pollutant concentration for the next time step; S6. Repeat steps S2 to S5, calculate separately for all sub-watersheds and all hydrological response units, record the time series of the updated amount of soil pollutant concentration and the actual amount of pollutants scoured within the simulation period until the end of the simulation period, and calculate the agricultural non-point source load within the target watershed within the simulation period by using the time series of the updated amount of soil pollutant concentration and the actual amount of pollutants scoured within the simulation period of all sub-watersheds and all agricultural-related hydrological response units and the corresponding surface runoff time series data.

[0008] Specifically, in step S2, the calculation formula for the scourable soil thickness is: , In the formula: is the scourable soil thickness, is the soil thickness, is the surface runoff, is the simulation coefficient, is the runoff depth for scouring 50% of the potential pollutants, represents the current time step.

[0009] Specifically, in step S3, the calculation formula for the amount of pollutants scourable per unit area is: , In the formula: is the amount of pollutants scourable per unit area, is the updated amount of soil pollutant concentration at the previous time step, and for the first time step is the initial soil pollutant concentration, is the background concentration of soil pollutants.

[0010] Specifically, in step S4, the calculation formula for the amount of pollutants actually scoured per unit area is: , In the formula: is the amount of pollutants actually scoured per unit area, is the surface runoff required to wash away 90% of the surface accumulated pollutants.

[0011] Specifically, in step S5, the calculation formula for the first-order degradation amount of soil pollutant concentration is: , In the formula: is the first-order degradation amount of soil pollutant concentration, is the first-order degradation coefficient of pollutants.

[0012] Specifically, in step S5, the calculation formula for the updated amount of soil pollutant concentration is: , In the formula: is the updated amount of soil pollutant concentration, is the amount of newly added pollutants.

[0013] Specifically, in step S6, the agricultural non-point source load includes the agricultural non-point source surface scouring load and the agricultural non-point source surface storage load. The calculation formula for the agricultural non-point source surface scouring load is: , In the formula: is the agricultural non-point source surface scouring load, is the area of this hydrological response unit in this sub-catchment area; The calculation formula for the agricultural non-point source surface storage load is: , In the formula: is the surface storage load of agricultural non-point source; The agricultural non-point source loads of each sub-watershed are accumulated to obtain the non-point source loads of each agricultural-related hydrological response unit in the target watershed during the simulation period.

[0014] Specifically, in step S1, the surface runoff time series data of different hydrological response units in the watershed are obtained through the following steps: Obtain the elevation DEM data, land use type data, soil data and historical meteorological data of the target watershed, construct a watershed hydrological and water quality model, and 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-watershed within the watershed.

[0015] Specifically, the soil data includes soil type and soil physical and chemical properties; the elevation DEM data is used to extract the watershed scope and calculate topographic features; the historical meteorological data includes multiple of rainfall, temperature, humidity, wind direction, wind speed, air pressure, evaporation, cloud cover, sunshine and solar radiation.

[0016] Specifically, the construction steps of the watershed hydrological and water quality model include: selecting a watershed hydrological and water quality simulation calculation platform matching the target watershed, and dividing sub-watersheds according to the elevation DEM data; converting the elevation DEM data into slope spatial vector data, fusing the land use type data, soil data and slope spatial vector data to construct hydrological response units; simulating the macroscopic hydrological process and macroscopic water quality process, where simulating the macroscopic hydrological process refers to simulating the hydrological cycle of a small watershed, including simulating rainfall interception, infiltration, evapotranspiration, surface runoff and the runoff generation and confluence processes within the region; simulating the macroscopic water quality process includes simulating the processes of pollutant accumulation, scouring, degradation and transport; verifying the preliminary operation results of the watershed hydrological and water quality model according to the regional runoff coefficient and annual average pollutant concentration survey data.

[0017] Beneficial effects: Compared with the prior art, the remarkable effects of the present invention are: 1. Refined expression of background concentration of soil pollutants: The present invention targets different types of land in the target watershed, uses hydrological response units as the smallest division unit, and adopts a differential expression of background concentration of soil pollutants, which can predict the pollutant scouring concentration caused by rainfall without additional pollution sources, more truly reflect the influence of original pollutants on different types of land, and thus improve the simulation accuracy of pollutant concentration in runoff.

[0018] 2. Variation of fertilization process by dynamically simulating soil pollutant concentration: Considering different soil types, crop species, and fertilization methods, pollutants are applied to soils at different depths, and the amount of eroded soil is dynamically calculated according to the precipitation runoff during the scouring process, enabling more accurate simulation of the interaction between precipitation and pollutant scouring.

[0019] 3. Variation of degradation process by dynamically simulating soil pollutant concentration: For different fertilizer types and soil characteristics, the degradation process of pollutants in the soil under the actions of microbial activities and crop absorption is simulated, making the pollutant removal process more consistent with the natural attenuation process in the ecosystem and greatly improving the authenticity and accuracy of the model's simulation of the pollutant removal process.

[0020] 4. Variation of scouring process by dynamically simulating soil pollutant concentration: For different soil characteristics and runoff depths, the process of the change in the amount of pollutants in the soil with the change in runoff depth is simulated. The simulation of fertilizer application is no longer limited to surface application but can apply fertilizers to soils at different depths according to the needs of crop roots and the adsorption characteristics of the soil. In this way, it can more accurately reflect the corresponding changes in the pollutant concentrations in soils at different depths with the change in fertilization depth, thereby more accurately simulating the pollutant scouring amounts in soils at different depths during the scouring process. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is the flowchart of the method of the present invention.

[0022] Figure 2 is the time series diagram of surface runoff in the basin of Embodiment 1 of the present invention.

[0023] Figure 3 is the time series comparison diagram of the surface scouring load of agricultural non-point sources in the basin of Embodiment 1 of the present invention.

[0024] Figure 4 is the time series comparison diagram of the surface storage load of agricultural non-point sources in the basin of Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] The following further describes a preferred solution of the present invention with reference to the drawings.

[0026] Please refer to Figure 1 as shown, the present invention provides an agricultural non-point source load simulation calculation method, including the following steps: S1. To establish a computational model, it is first necessary to obtain the modeling data within the target basin, including elevation DEM data, land use type data, soil data, and historical meteorological data. The soil data includes soil type and soil physical and chemical properties; the elevation DEM data is used to extract the basin scope and calculate topographic features; the land use type data refers to land resource units with the same land use method, which are divided according to the regional differences in land use and are the basic regional units reflecting land use, nature, and its distribution law, such as paddy fields, dry land, orchards, rural areas, towns, waters, wetlands, roads, etc.; the historical meteorological data includes multiple of rainfall, temperature, humidity, wind direction, wind speed, air pressure, evaporation, cloud cover, sunshine, and solar radiation. The above modeling data is subjected to spatial overlay, coordinate system unification, and format conversion processing, and a comprehensive modeling database for the target basin is constructed based on the sorted modeling data.

[0027] After completing the processing of the modeling data, a basin hydrological and water quality model is constructed. A basin hydrological and water quality simulation calculation platform (such as SWAT, HSPF, LSPC, MIKE SHE) that matches the target basin is selected. Sub-watersheds are delineated according to the elevation DEM data; the elevation DEM data is converted into slope spatial vector data, and the land use type data, soil data, and slope spatial vector data are fused to construct hydrological response units; the macroscopic hydrological process and macroscopic water quality process are simulated. Among them, simulating the macroscopic hydrological process refers to simulating the hydrological cycle of small basins, including simulating rainfall interception, infiltration, evapotranspiration, surface runoff, and runoff generation and concentration processes within the region; simulating the macroscopic water quality process includes simulating pollutant accumulation, scour, degradation, and transport processes; according to the regional runoff coefficient and annual average pollutant concentration survey data, the preliminary operation results of the basin hydrological and water quality model are verified to ensure the rationality of the model simulation.

[0028] The surface runoff time series data of different agriculture-related hydrological response units within each sub-watershed in the basin is calculated and output using the basin hydrological and water quality model. In this embodiment, the time step is 1 h.

[0029] Existing basin hydrological and water quality models do not fully consider some key natural processes when simulating agricultural non-point source loads: (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 pollutant migration caused by runoff scour and completely do not incorporate the nutrient absorption process and microbial metabolism during the crop growth cycle into the calculation framework, resulting in the simulation results being unable to truly reflect the dynamic cycle of pollutants in the soil-plant-atmosphere system.

[0030] (2) Idealized assumptions in the fertilization process: In agricultural fertilization simulation, there are generally three major idealized defects in the models: Simplification of surface fertilization treatment: All fertilizers are mechanically applied to the soil surface, ignoring the impact of diverse fertilization methods such as deep fertilization and side fertilization on the vertical distribution of pollutants in actual production.

[0031] Assumption of complete pollutant migration: It is assumed that surface pollutants can be completely washed away by rainfall runoff, without considering the adsorption and interception of pollutants by different soil depth layers.

[0032] Static state of soil vertical profile: The pollutant concentration is regarded as existing only in the surface layer, without reflecting the accumulation and migration of pollutants in deep soil, which is contrary to the actual law of pollutant infiltration into different soil layers with water.

[0033] (3) Simplification of key driving factors: The following key influencing factors are not considered in the model during the simulation of the fertilization process: Difference in fertilization depth: Different fertilization methods (such as deep application of base fertilizer and shallow application of top dressing) lead to different initial distributions of pollutants in the soil, thereby affecting their migration paths and loss risks.

[0034] Heterogeneity of soil types: The adsorption capacities of different soil textures such as clay and sand for pollutants vary significantly, but this is not incorporated into the calculation in the model.

[0035] Crop types and growth stages: The intensity of nutrient requirements and the characteristics of root distribution of different crops determine the fertilization depth and the bioavailability of pollutants, but this dynamic process is not reflected in the model.

[0036] To solve the above problems, the present invention improves the original agricultural non-point source load module in the watershed hydrological and water quality model and adds a series of new model parameters: (1) Add a differential expression of pollutant background concentration; (2) Add a first-order degradation expression of pollutant concentration in the soil; (3) Add a dynamic change expression of the amount of pollutants in the soil under different runoff depths; (4) Add an expression of the change in pollutant concentration at different depths in the soil.

[0037] 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-watershed within the target watershed; sets the model parameters corresponding to different agricultural-related hydrological response units within each group of sub-watersheds, including the initial soil pollutant concentration (mg / L), the amount of newly added 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 runoff depth for scouring 50% of potential pollutants (inch), the surface runoff volume required to wash away 90% of the surface-accumulated pollutants (inch / hour), and the adjustment coefficient.

[0038] S2. Select the simulation period, read the surface runoff at the first time step within the simulation period from the surface runoff time series data of a certain agricultural-related hydrological response unit in a certain sub-watershed, that is, the surface runoff within the first hour of the first day, and calculate the soil thickness that can be scoured within the current time step of this hydrological response unit by using the corresponding soil thickness, the runoff depth for scouring 50% of potential pollutants, and the adjustment coefficient: , In the formula: is the soil thickness that can be scoured within the current time step, with the unit of m / hour, is the soil thickness, is the surface runoff within the current time step, is the simulation coefficient, is the runoff depth for scouring 50% of potential pollutants, represents the current time step.

[0039] S3. Calculate the amount of pollutants that can be scoured per unit area of this hydrological response unit within the current time step by using the scourable soil thickness and the corresponding initial soil pollutant concentration and background concentration of soil pollutants: , In the formula: is the amount of pollutants scoured per unit area, with the unit of lb / acre / hour, is the updated amount of soil pollutant concentration at the previous time step, and for the first time step is the initial soil pollutant concentration, is the background concentration of soil pollutants, is the unit conversion coefficient.

[0040] S4. Calculate the actual amount of pollutants scoured per unit area of this hydrological response unit within the current time step by using the amount of pollutants scoured per unit area and the corresponding surface runoff volume required to wash away 90% of the surface-accumulated pollutants: , where: is the amount of pollutants actually scoured per unit area, is the surface runoff required to wash away 90% of the accumulated surface pollutants.

[0041] 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, use the newly added pollutant amount, the amount of pollutants actually scoured per unit area, the first-order degradation amount of soil pollutant concentration, and the soil thickness to calculate the updated amount of soil pollutant concentration in this hydrological response unit during the current time step. Take the updated amount of soil pollutant concentration at the current time step as the initial soil pollutant concentration for the next time step.

[0042] 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 pollutants.

[0043] The calculation formula for the updated amount of soil pollutant concentration is: , where: is the updated amount of soil pollutant concentration, is the newly added pollutant amount, is the unit conversion coefficient.

[0044] S6. Repeat steps S2 to S5 to calculate each hydrological response unit separately, and record the time series of the updated amount of soil pollutant concentration during the simulation period until the end of the simulation period. Use the time series of the updated amount of soil pollutant concentration and the corresponding surface runoff time series data of all hydrological response units during the simulation period to calculate the agricultural non-point source load in the target watershed during the simulation period.

[0045] The agricultural non-point source load includes the agricultural non-point source surface scouring load and the agricultural non-point source surface storage load. The calculation formula for the agricultural non-point source surface scouring load is: , where: is the agricultural non-point source surface scouring load, is the area of the hydrological response unit; The calculation formula for the agricultural non-point source surface storage load is: , where: is the agricultural non-point source surface storage load, is the unit conversion coefficient; Accumulate the agricultural non-point source loads of each sub-watershed in the target watershed to obtain the non-point source loads of each agricultural-related hydrological response unit in the target watershed during the simulation period.

[0046] Embodiment 1 In this embodiment, the statistical data of a certain watershed in 2023 is substituted into the above calculation method for statistics and calculation. Figure 2 is the time series diagram of surface runoff in a certain sub-watershed within the watershed. To prove the implementation effect of this solution, the original agricultural non-point source load module is used to calculate the statistical data, and the calculation results are compared with those obtained by using the improved agricultural non-point source load module of the present invention. Figure 3 is the time series diagram of the agricultural non-point source surface scouring load in a certain sub-watershed within the watershed, Figure 3 The yellow line in it represents the time series of the agricultural non-point source surface scouring load calculated by using the original agricultural non-point source load module, Figure 3 The blue line in it represents the time series of the agricultural non-point source surface scouring load calculated by adding the improved agricultural non-point source load module of the present invention; it can be intuitively seen that throughout the year, the data of the agricultural non-point source surface scouring load obtained by using the calculation method provided by the present invention is significantly lower than the data of the agricultural non-point source surface scouring load obtained by the traditional calculation method that only considers the surface runoff scouring effect. This result is consistent with the trend of the above theoretical analysis result and is more in line with the objective law; Figure 4 is the time series diagram of the agricultural non-point source surface storage load in a certain sub-watershed within the watershed; Figure 4 The yellow curve in it represents the time series of the agricultural non-point source surface storage load calculated by using the original agricultural non-point source load module, Figure 4 The blue curve in it 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 intuitively seen that throughout the year, the data of the agricultural non-point source surface storage load obtained by using the calculation method provided by the present invention is higher than the data of the agricultural non-point source surface storage load obtained by the traditional calculation method that only considers the surface runoff scouring effect. This result is also consistent with the trend of the above theoretical analysis result and is more in line with the objective law.

Claims

1. An agricultural non-point source load simulation and calculation method, characterized in that It includes the following steps: S1. Construct 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 at the first time step within the simulation period from the surface runoff time series data of a certain hydrological response unit, and calculate the thickness of the soil that can be scoured within the current time step of this hydrological response unit by using the corresponding model parameters; S3. Calculate the amount of pollutants that can be scoured per unit area of this hydrological response unit within the current time step by using the thickness of the soil that can be scoured and the corresponding model parameters; S4. Calculate the actual amount of pollutants scoured per unit area of this hydrological response unit within the current time step by using the amount of pollutants that can be scoured per unit area and the corresponding model parameters; S5. Calculate the first-order degradation amount of the soil pollutant concentration by using the model parameters, and then calculate the updated amount of the soil pollutant concentration of this hydrological response unit within the current time step by using the actual amount of pollutants scoured per unit area, the first-order degradation amount of the soil pollutant concentration and the model parameters, and use the updated amount of the soil pollutant concentration at the current time step as the initial soil pollutant concentration at the next time step; S6. Repeat steps S2 to S5, calculate for all hydrological response units respectively, record the time series data of the updated amount of the soil pollutant concentration and the actual amount of pollutants scoured during the simulation period until the end of the simulation period, and calculate the agricultural non-point source load of the target watershed during the simulation period by using the time series data of the updated amount of the soil pollutant concentration, the actual amount of pollutants scoured and the corresponding surface runoff time series data of all hydrological response units during the simulation period.

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

3. The agricultural non-point source load simulation calculation method according to claim 2, characterized in that: In the above step S2, the calculation formula for the thickness of the soil that can be scoured is: ， Wherein: is the thickness of the erodible soil, is the soil thickness, is the surface runoff, is the simulation coefficient, is the runoff depth of the potential 50% erodible pollutants, represents the current time step.

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

5. The agricultural non-point source load simulation calculation method according to claim 4, wherein: In the above step S4, the calculation formula for the actual amount of pollutants scoured per unit area is: ， In the formula: is the amount of pollutants actually scoured per unit area, is the surface runoff required to wash away 90% of the surface-accumulated pollutants.

6. The agricultural non-point source load simulation calculation method according to claim 5, wherein: In the above step S5, the calculation formula for the first-order degradation amount of the soil pollutant concentration is: ， In the formula: is the first-order degradation amount of the soil pollutant concentration, is the first-order degradation coefficient of the pollutant.

7. The agricultural non-point source load simulation and calculation method according to claim 6, characterized in that: In the above step S5, the calculation formula for the updated amount of the soil pollutant concentration is: ， In the formula: is the updated amount of soil pollutant concentration, is the amount of newly added pollutant.

8. The agricultural non-point source load simulation calculation method according to claim 7, wherein: In the above step S6, the agricultural non-point source load includes the agricultural non-point source surface scouring load amount and the agricultural non-point source surface storage load amount. The calculation formula for the agricultural non-point source surface scouring load amount is: ， In the formula: is the surface scouring load of agricultural non-point source; is the area of the hydrological response unit; The calculation formula for the agricultural non-point source surface storage load amount is: ， Wherein: is the surface storage load of agricultural non-point source; Finally, obtain the agricultural non-point source load of each hydrological response unit in the target watershed during the simulation period.

9. The agricultural non-point source load simulation calculation method according to claim 1, characterized in that: In the above step S1, the surface runoff time series data of different hydrological response units in the watershed are obtained through the following steps: Obtain the elevation DEM data, land use type data, soil data, and historical meteorological data of the target basin, construct a basin hydrological and water quality model, and use the basin hydrological and water quality model to calculate and output the surface runoff time series data of different hydrological response units within the basin.

10. The agricultural non-point source load simulation calculation method according to claim 9, characterized in that: The soil data includes soil type and soil physical and chemical properties; the elevation DEM data is used to extract the basin scope and calculate topographic features; the historical meteorological data includes multiple of rainfall, temperature, humidity, wind direction, wind speed, air pressure, evaporation, cloud cover, sunshine, and solar radiation; The construction steps of the basin hydrological and water quality model include: selecting a basin hydrological and water quality simulation calculation platform that matches the target basin, converting the elevation DEM data into slope spatial vector data, fusing the land use type data, soil data, and slope spatial vector data to construct hydrological response units; simulating the macroscopic hydrological process and macroscopic water quality process, where simulating the macroscopic hydrological process refers to simulating the hydrological cycle of a small basin, including simulating rainfall interception, infiltration, evapotranspiration, surface runoff, and the runoff generation and confluence processes within the region; simulating the macroscopic water quality process includes simulating the processes of pollutant accumulation, scouring, degradation, and transport; verifying the preliminary operation results of the basin hydrological and water quality model according to the regional runoff coefficient and annual average pollutant concentration survey data.

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