Estimation method and system for field rainfall runoff scouring non-point source pollution load

Abstract

The application provides a field rainfall runoff scouring non-point source pollution load estimation method and system, comprising: obtaining target basin basic data; defining non-point source pollution, dividing time periods and dividing land use types according to target basin characteristics; constructing an ICM comprehensive basin model according to the collected basic data; calculating different rainfall characteristic design storms according to historical rainfall data or a hydrology manual; forming characteristic factor groups according to different rainfall characteristic design storms and pre-rain drought periods; obtaining non-point source pollution loads of different characteristic factors according to a design storm driven model; constructing a response relationship between non-point source pollution loads and different characteristic factors; identifying measured field rainfall characteristic factors, and obtaining measured field rainfall non-point source pollution loads according to the response relationship. The application has a clear physical mechanism of rainfall runoff and non-point source pollution formation processes, and can accurately calculate basin non-point source pollution loads after calibration and verification of measured data.

Description

Technical Field

[0001] This invention relates to the technical field of environmental pollution control, specifically to a method and system for estimating the non-point source pollution load from rainfall runoff, and more particularly to a rapid method for estimating the non-point source pollution load from rainfall runoff. Background Technology

[0002] Pollution load estimation is fundamental and crucial for implementing water environment quality control and water resource protection and utilization. In recent years, with the gradual improvement of pollution management in my country, point source pollution control has achieved significant results. However, non-point source pollution, due to difficulties in monitoring and its characteristics of uncertainty, randomness, and widespread occurrence, has increasingly become a key focus and challenge in water environment quality control and water resource protection and utilization. Therefore, systematically estimating regional non-point source pollution loads has become an unavoidable and important research topic. Among these, rapidly estimating the non-point source pollution load from rainfall runoff is key to achieving dynamic water environment quality management and rapid response to regional water pollution prevention and control emergency mechanisms. Therefore, there is an urgent need to propose a rapid estimation method for the non-point source pollution load from rainfall runoff in a target watershed.

[0003] Currently, commonly used methods for estimating non-point source pollution loads both domestically and internationally mainly include output coefficient models, empirical models, and mechanistic models. Among these, output coefficient models and empirical models are relatively simple to calculate, but they have shortcomings in terms of estimation accuracy and portability, and they lack the ability to describe the physical mechanisms of non-point source pollution formation. Mechanistic models use mathematical models to simulate the formation of rainfall runoff and the migration and transformation processes of pollutants to estimate pollution loads, but they have high data requirements, numerous parameters, and complex operations, making them unsuitable for rapid pollution load estimation.

[0004] In existing non-point source pollution simulation studies, the SWAT (Soil and Water Assessment Tool) and AGNPS (Agricultural Non-point Source) models are commonly used for simulating non-point source pollution in agricultural areas, while the SWMM (Storm Water Management Model) model is used for simulating non-point source pollution in urban areas. Currently, there is a lack of methods that consider integrated watershed non-point source pollution simulation.

[0005] Meanwhile, current studies on non-point source pollution estimation typically use years or months as the time scale, without taking into account in detail the non-point source pollution load caused by rainfall runoff. However, non-point source pollution caused by rainfall runoff is an important aspect of real-time water environment quality management.

[0006] In summary, current methods for estimating non-point source pollution loads have limitations, lack comprehensive watershed non-point source pollution simulation methods, and cannot meet the need for real-time estimation of non-point source pollution loads at the rainfall scale.

[0007] A patent document with publication number CN104951986B discloses a method for estimating the load of agricultural non-point source pollutants entering lakes in a watershed, including: acquiring data of typical small watersheds in the target watershed, constructing a SWAT model for the typical small watersheds, and obtaining the load in the typical small watersheds based on the SWAT model. Lr and Load Lo ; Obtain the pollution discharge coefficients of crop farming, livestock and poultry farming, aquaculture, and rural domestic sources in each sub-basin of a typical small watershed; combine this with basic information survey data to obtain the Load in the typical small watershed. Ls According to Load Ls and Load Lr Obtain the Ditch Reduction Factor for Agricultural Non-point Source Pollutants in the Target Watershed cr ; Obtain Length based on water system data Lr According to Load Lr Load Lo and Length Lr Obtain the river channel reduction coefficient (Factor) for agricultural non-point source pollutants in the target watershed. rr Obtain the agricultural non-point source nitrogen and phosphorus emission load and the total length of rivers within the target watershed. Br Based on the agricultural non-point source nitrogen and phosphorus emission load of the target watershed, Factor cr and Factor rr and Length Br To obtain the inflow load of agricultural non-point source pollutants into the lake in the target watershed.

[0008] Therefore, a new technical solution is needed to improve the above-mentioned technical problems. Summary of the Invention

[0009] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for estimating the non-point source pollution load caused by rainfall runoff.

[0010] According to the present invention, a method for estimating the non-point source pollution load of a rainfall runoff event includes the following steps:

[0011] Step S1: Obtain basic data for the target watershed;

[0012] Step S2: Define non-point source pollution, classify time periods, and classify land use types based on the characteristics of the target watershed;

[0013] Step S3: Construct an ICM integrated watershed model based on the collected basic data, and calibrate the model using measured rainfall-runoff and water quality data;

[0014] Step S4: Calculate different rainfall characteristics and design rainstorms based on historical rainfall data or rainstorm intensity formulas, and calculate and design rainstorm processes based on typical rainstorms or design rainfall patterns;

[0015] Step S5: Design characteristic factor groups for heavy rain and pre-rain drought periods based on different rainfall characteristics;

[0016] Step S6: Based on the designed rainstorm-driven model, obtain the area source pollution load of different characteristic factors;

[0017] Step S7: Construct the response relationship between non-point source pollution load and different characteristic factors;

[0018] Step S8: Identify the characteristic factors of the measured rainfall events, and obtain the non-point source pollution load of the measured rainfall events based on the response relationship between the characteristic factors and the non-point source pollution load.

[0019] Preferably, the basic data of the target watershed in step S1 includes topographic data, drainage system data, river cross-section data, measured rainfall runoff and water quality data;

[0020] The topographic data, drainage system data, and river cross-section data are used in constructing the watershed non-point source pollution numerical model, while the measured rainfall, runoff, and water quality data are used in model parameter calibration and validation.

[0021] Preferably, step S2 defines non-point source pollution as rural domestic pollution, free-range livestock and poultry breeding pollution, agricultural non-point source pollution, and urban rainfall runoff pollution based on watershed characteristics; divides the simulation period into fertilization period and non-fertilization period; and classifies land use types into cultivated land, orchard, forest and grassland, construction land, and other land.

[0022] Preferably, the ICM model in step S3 expresses the non-point source pollution formation process as a pollutant accumulation process, a pollutant flushing process, and a pollutant cleaning process.

[0023] Preferably, the rainfall characteristics in step S4 include rainfall magnitude, rainfall pattern, and rainfall duration, wherein the rainfall magnitude is expressed by the return period, and the rainfall pattern is expressed by the peak rainfall coefficient.

[0024] The present invention also provides a system for estimating the non-point source pollution load of rainfall runoff, the system comprising the following modules:

[0025] Module M1: Obtain basic data for the target watershed;

[0026] Module M2: Defines non-point source pollution, classifies time periods, and classifies land use types based on the characteristics of the target watershed;

[0027] Module M3: Constructs an ICM integrated watershed model based on collected basic data, and calibrates the model using measured rainfall, runoff, and water quality data;

[0028] Module M4: Calculates different rainfall characteristics and designs rainstorms based on historical rainfall data or rainstorm intensity formulas, and calculates and designs rainstorm processes based on typical rainstorms or design rainfall patterns;

[0029] Module M5: Design characteristic factor groups for heavy rain and pre-rain drought periods based on different rainfall characteristics;

[0030] Module M6: Based on the designed rainstorm-driven model, obtain the area source pollution load of different characteristic factors;

[0031] Module M7: Constructing the response relationship between non-point source pollution load and different characteristic factors;

[0032] Module M8: Identifies the characteristic factors of measured rainfall events and obtains the non-point source pollution load of measured rainfall events based on the response relationship between the characteristic factors and the non-point source pollution load.

[0033] Preferably, the target watershed basic data in module M1 includes topographic data, drainage system data, river cross-section data, measured rainfall runoff and water quality data;

[0034] The topographic data, drainage system data, and river cross-section data are used in constructing the watershed non-point source pollution numerical model, while the measured rainfall, runoff, and water quality data are used in model parameter calibration and validation.

[0035] Preferably, module M2 defines non-point source pollution as rural domestic pollution, free-range livestock and poultry breeding pollution, agricultural non-point source pollution, and urban rainfall runoff pollution based on watershed characteristics; divides the simulation period into fertilization period and non-fertilization period; and classifies land use types into cultivated land, orchard, forest and grassland, construction land, and other land.

[0036] Preferably, the ICM model in module M3 expresses the non-point source pollution formation process as a pollutant accumulation process, a pollutant flushing process, and a pollutant cleaning process.

[0037] Preferably, the rainfall characteristics in module M4 include rainfall magnitude, rainfall pattern, and rainfall duration, wherein rainfall magnitude is expressed by return period, and rainfall pattern is expressed by peak rainfall coefficient.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. This invention has a clear physical mechanism for the formation of rainfall runoff and non-point source pollution. After calibration and verification with measured data, it can accurately calculate the non-point source pollution load of the watershed.

[0040] 2. This invention, through the definition of non-point source pollution, period division, and land use type division, can realize the simulation of non-point source pollution load in a comprehensive watershed, and has good adaptability to the environmental differences in non-point source pollution in different watersheds;

[0041] 3. By identifying characteristic factors, this invention can quickly estimate the non-point source pollution load caused by rainfall runoff based on the constructed relationship between non-point source pollution and characteristic factor response. Attached Figure Description

[0042] 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:

[0043] Figure 1 This is a technical roadmap for the present invention;

[0044] Figure 2 This is a flowchart illustrating the modeling process of the present invention.

[0045] Figure 3 This invention designs schematic diagrams of rainstorms with different rainfall characteristics;

[0046] Figure 4 This is a flowchart illustrating the estimation of non-point source pollution load based on measured rainfall events in this invention. Detailed Implementation

[0047] 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.

[0048] Example 1:

[0049] According to the present invention, a method for estimating the non-point source pollution load of a rainfall runoff event includes the following steps:

[0050] Step S1: Obtain basic data for the target watershed; the basic data for the target watershed includes topographic data, drainage system data, river cross-section data, measured rainfall runoff and water quality data; topographic data, drainage system data, and river cross-section data are used in constructing the watershed non-point source pollution numerical model, and measured rainfall runoff and water quality data are used in model parameter calibration and validation.

[0051] Step S2: Define non-point source pollution, divide periods, and classify land use types according to the characteristics of the target watershed; based on the characteristics of the watershed, define non-point source pollution as rural domestic pollution, free-range livestock and poultry breeding pollution, agricultural non-point source pollution, and urban rainfall runoff pollution; divide the simulation period into fertilization period and non-fertilization period; classify land use types into cultivated land, orchard, forest and grassland, construction land, and other land.

[0052] Step S3: Construct an ICM integrated watershed model based on the collected basic data, and calibrate the model using measured rainfall, runoff and water quality data; the ICM model expresses the non-point source pollution formation process as a pollutant accumulation process, a pollutant flushing process and a pollutant cleanup process.

[0053] Step S4: Calculate different rainfall characteristics to design rainstorms based on historical rainfall data or rainstorm intensity formulas, and calculate the rainstorm process based on typical rainstorms or design rainstorm patterns; rainfall characteristics include rainfall magnitude, rainstorm pattern and rainfall duration, where rainfall magnitude is expressed by return period and rainstorm pattern is expressed by peak rainfall coefficient.

[0054] Step S5: Design characteristic factor groups for heavy rain and pre-rain drought periods based on different rainfall characteristics;

[0055] Step S6: Based on the designed rainstorm-driven model, obtain the area source pollution load of different characteristic factors;

[0056] Step S7: Construct the response relationship between non-point source pollution load and different characteristic factors;

[0057] Step S8: Identify the characteristic factors of the measured rainfall events, and obtain the non-point source pollution load of the measured rainfall events based on the response relationship between the characteristic factors and the non-point source pollution load.

[0058] The present invention also provides a system for estimating the non-point source pollution load of rainfall runoff. The system can be implemented by executing the process steps of the method for estimating the non-point source pollution load of rainfall runoff. That is, those skilled in the art can understand the method for estimating the non-point source pollution load of rainfall runoff as a preferred embodiment of the system for estimating the non-point source pollution load of rainfall runoff.

[0059] Example 2:

[0060] The present invention also provides a system for estimating the non-point source pollution load of rainfall runoff, the system comprising the following modules:

[0061] Module M1: Acquire basic data of the target watershed; the basic data of the target watershed includes topographic data, drainage system data, river cross-section data, measured rainfall runoff and water quality data; topographic data, drainage system data, and river cross-section data are used in constructing the watershed non-point source pollution numerical model, and measured rainfall runoff and water quality data are used in model parameter calibration and validation.

[0062] Module M2: Defines non-point source pollution, divides periods, and classifies land use types according to the characteristics of the target watershed; based on the characteristics of the watershed, non-point source pollution is defined as rural domestic pollution, free-range livestock and poultry breeding pollution, agricultural non-point source pollution, and urban rainfall runoff pollution; the simulation period is divided into fertilization period and non-fertilization period; and land use types are divided into cultivated land, orchard, forest and grassland, construction land, and other land.

[0063] Module M3: Constructs an ICM integrated watershed model based on the collected basic data, and calibrates the model using measured rainfall, runoff and water quality data; the ICM model expresses the non-point source pollution formation process as a pollutant accumulation process, a pollutant flushing process and a pollutant cleanup process.

[0064] Module M4: Calculates different rainfall characteristics to design rainstorms based on historical rainfall data or rainstorm intensity formulas, and calculates and designs rainstorm processes based on typical rainstorms or design rainstorm patterns; rainfall characteristics include rainfall magnitude, rainstorm pattern, and rainfall duration, where rainfall magnitude is expressed by return period and rainstorm pattern is expressed by peak rainfall coefficient.

[0065] Module M5: Design characteristic factor groups for heavy rain and pre-rain drought periods based on different rainfall characteristics;

[0066] Module M6: Based on the designed rainstorm-driven model, obtain the area source pollution load of different characteristic factors;

[0067] Module M7: Constructing the response relationship between non-point source pollution load and different characteristic factors;

[0068] Module M8: Identifies the characteristic factors of measured rainfall events and obtains the non-point source pollution load of measured rainfall events based on the response relationship between the characteristic factors and the non-point source pollution load.

[0069] Example 3:

[0070] Example 3 is a preferred example of Example 1, and is used to illustrate the present invention in more detail.

[0071] Based on the ICM integrated watershed model and mathematical statistics, this invention proposes a rapid estimation method for non-point source pollution load by rainfall runoff in a single event, through the definition of non-point source pollution, period division, and land use type division. While having a clear non-point source pollution formation mechanism, it achieves rapid estimation of non-point source pollution load by rainfall runoff in a single event in an integrated watershed.

[0072] Reference Figure 1 To address the aforementioned problems, this invention proposes a rapid method for estimating the non-point source pollution load from rainfall runoff, the main steps of which are as follows:

[0073] S1: Obtain basic data for the target watershed, mainly including: topographic data, drainage system data, river cross-section data, measured rainfall runoff and water quality data;

[0074] S2: Define non-point source pollution, classify its time periods, and classify land use types based on the characteristics of the target watershed;

[0075] S3: Construct an ICM integrated watershed model based on the collected basic data, and calibrate the model using measured rainfall, runoff, and water quality data;

[0076] S4: Calculate different rainfall characteristics and design rainstorms based on historical rainfall data or rainstorm intensity formulas; calculate and design rainstorm processes based on typical rainstorms or design rainfall patterns.

[0077] S5: Design characteristic factor groups for heavy rain and pre-rain drought periods based on different rainfall characteristics;

[0078] S6: Based on the designed rainstorm-driven model, obtain the area source pollution load of different characteristic factors;

[0079] S7: Construct the response relationship between non-point source pollution load and different characteristic factors;

[0080] S8: Identify the characteristic factors of the measured rainfall events, and obtain the non-point source pollution load of the measured rainfall events based on the response relationship between the characteristic factors and the non-point source pollution load.

[0081] The basic data for the target watershed are mainly obtained, including: topographic data, drainage system data, river cross-sectional data, measured rainfall-runoff and water quality data. Among them, the topographic data, drainage system data, and river cross-sectional data are used to construct a numerical model of watershed non-point source pollution, while the measured rainfall-runoff and water quality data are used for model parameter calibration and validation.

[0082] Based on the characteristics of the target watershed, non-point source pollution was defined, time periods were divided, and land use types were classified. According to the watershed characteristics, non-point source pollution was defined as rural domestic pollution, free-range livestock and poultry farming pollution, agricultural non-point source pollution, and urban rainfall runoff pollution. The simulation period was divided into fertilization period and non-fertilization period. Land use types were classified as cultivated land, orchards, forest and grassland, construction land, and other land.

[0083]

[0084] An integrated watershed model (ICM) was constructed based on collected basic data. The ICM model expresses the non-point source pollution formation process as a pollutant accumulation process, a pollutant flushing process, and a pollutant cleanup process. Among these:

[0085] Pollutant accumulation models are available:

[0086] Power function:

[0087]

[0088] In the formula, C1 is the maximum possible accumulation; C2 is the accumulation rate constant; C3 is the time exponent; and t is the number of drought days in the preceding period.

[0089] Exponential function:

[0090]

[0091] In the formula, C1 is the maximum possible accumulation; C2 is the accumulation rate constant; and t is the number of drought days in the preceding period.

[0092] Saturation function:

[0093]

[0094] In the formula, C1 is the maximum possible cumulative amount; C2 is the half-saturation constant; and t is the number of drought days in the preceding period.

[0095] Pollutant flushing models are available:

[0096] Exponential equation:

[0097]

[0098] In the formula, C1 is the scour coefficient; C2 is the scour index; q is the runoff per unit area; and B is the accumulated pollutants.

[0099] Flow characteristics scour curve:

[0100]

[0101] In the formula, C1 is the scouring coefficient; C2 is the scouring index; and Q is the runoff rate in the user-defined flow unit.

[0102] Average concentration of rainfall: This is a special case of grade curve erosion, where the index is 1.0 and the coefficient C1 represents the concentration of pollutants washed down.

[0103] Cleaning mode: Reduces the accumulation of specific contaminants through regular cleaning, including two parameters: cleaning interval and cleaning removal rate.

[0104] Reference Figure 2In the modeling process, cultivated land was further divided into paddy fields and dry land, and construction land was divided into rural residential areas, urban residential areas, commercial areas, industrial areas, and public service areas. Then, based on the regional non-point source pollution type and land use type, sub-basins / sub-catchments were defined, and suitable pollutant accumulation, flushing models, and cleaning modes were selected for each sub-basin / sub-catchment. After completing the sub-basin / sub-catchment division, a pipe network model and a one-dimensional river channel model were constructed based on basic data to achieve comprehensive watershed non-point source pollution simulation. Finally, the model was calibrated using measured rainfall-runoff and water quality data during both fertilization and non-fertilization periods to determine the model parameters, including hydrological, hydrodynamic, and water quality modules.

[0105] Reference Figure 3 The design rainstorm is derived by considering different rainfall characteristics. The design rainstorm is calculated based on historical rainfall data or local rainstorm intensity formulas, and the design rainstorm process is calculated based on typical rainstorms or design rainfall patterns. The rainfall characteristics include: rainfall magnitude, rainfall pattern, and rainfall duration, where rainfall magnitude is expressed using return periods and rainfall pattern is expressed using peak rainfall coefficients.

[0106] Based on different rainfall characteristics, a characteristic factor group was designed to consist of heavy rainfall and pre-rain drought periods. The characteristic factor group includes rainfall duration, peak rainfall coefficient, return period, and pre-rain drought period.

[0107] X i =(t i ,r i ,T i A i (6)

[0108] In the formula, X i It is the i-th feature factor group; t i It is the i-th rainfall duration; r i It is the coefficient of the i-th rain peak; T i It is the i-th rainfall recurrence interval; A i It is the i-th pre-rain drought period.

[0109] Based on the designed storm-driven model, the non-point source pollution load under different characteristic factors is obtained. The non-point source pollution load of the target watershed is the sum of the non-point source pollution loads of each sub-watershed / sub-catchment.

[0110]

[0111] In the formula, Load T It is the total non-point source pollution load of the watershed; Load i,j It is the i-th characteristic factor group and the j-th sub-basin / sub-catchment non-point source pollution load.

[0112] The response relationship between non-point source pollution load and different characteristic factors was constructed. The response relationship between non-point source pollution load and characteristic factors was determined using methods such as multiple regression models and artificial neural networks.

[0113] Load i,T1 =f(X) i (8)

[0114] Load i,T2 =f(X) i (9)

[0115] In the formula, Load i,T1 This is the non-point source pollution load during the fertilization period under the i-th characteristic factor group; Load i,T2 X is the non-point source pollution load during the non-fertilization period under the i-th characteristic factor group; i It is the i-th feature factor group.

[0116] Reference Figure 4 The system identifies characteristic factors of measured rainfall events and obtains the non-point source pollution load of measured rainfall events based on the response relationship between these characteristic factors and non-point source pollution.

[0117] Load M =f(X) M (10)

[0118] In the formula, Load M This is the measured area source pollution load under rainfall event M; X M It is the characteristic factor group of the measured M rainfall events.

[0119] Those skilled in the art can understand this embodiment as a more specific description of Embodiment 1 and Embodiment 2.

[0120] 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.

[0121] 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 estimating the non-point source pollution load from rainfall runoff, characterized in that, The method includes the following steps: Step S1: Obtain basic data for the target watershed; Step S2: Define non-point source pollution, classify time periods, and classify land use types based on the characteristics of the target watershed; Step S3: Construct an ICM integrated watershed model based on the collected basic data, and calibrate the model using measured rainfall-runoff and water quality data; Step S4: Calculate different rainfall characteristics and design rainstorms based on historical rainfall data or rainstorm intensity formulas, and calculate and design rainstorm processes based on typical rainstorms or design rainfall patterns; Step S5: Design characteristic factor groups for heavy rain and pre-rain drought periods based on different rainfall characteristics; Step S6: Based on the designed rainstorm-driven model, obtain the area source pollution load of different characteristic factors; Step S7: Construct the response relationship between non-point source pollution load and different characteristic factors; Step S8: Identify the characteristic factors of the measured rainfall events, and obtain the non-point source pollution load of the measured rainfall events based on the response relationship between the characteristic factors and the non-point source pollution load; The rainfall characteristics in step S4 include rainfall magnitude, rainfall pattern, and rainfall duration, wherein rainfall magnitude is expressed by return period and rainfall pattern is expressed by peak rainfall coefficient. The ICM model in step S3 expresses the non-point source pollution formation process as a pollutant accumulation process, a pollutant flushing process, and a pollutant cleaning process. The pollutant accumulation model is any one of the following: Power function: Exponential function: Saturation function: In the formula, C 1 is the maximum possible cumulative amount; C 2 is the cumulative rate constant; C 3 represents the time index; C 4 is the half-saturation constant; t This refers to the number of days in the early drought period; The pollutant flushing model is any one of the following: Exponential equation: Flow characteristics scour curve: In the formula, C 5 is the scouring coefficient; C 6 is the scouring index; q It is the runoff per unit area; B It is accumulated pollutants; Q It is the runoff rate in the user-defined flow unit; The characteristic factor group includes rainfall duration, peak rainfall coefficient, return period, and pre-rain drought period; In the formula, X i It is the first i A set of characteristic factors; t i It is the first i The duration of the rainfall; r i It is the first i Rainfall peak coefficient; T i It is the first i One rainfall recurrence period; A i It is the first i A dry period before rain; Based on the designed storm-driven model, the non-point source pollution load under different characteristic factors is obtained. The non-point source pollution load of the target watershed is the sum of the non-point source pollution loads of each sub-watershed / sub-catchment: In the formula, Load T It is the total non-point source pollution load of the watershed; Load i,j It is the first i The feature factor group, the first j Non-point source pollution load in individual watersheds / catchment areas; The response relationship between non-point source pollution load and different characteristic factors was constructed, and the response relationship between non-point source pollution load and characteristic factors was determined by methods such as multiple regression model and artificial neural network. In the formula, Load i,T1 It is the first i Non-point source pollution load during fertilization period under a group of characteristic factors; Load i,T2 It is the first i Non-point source pollution load during the non-fertilization period under a group of characteristic factors; Identify the characteristic factors of measured rainfall events, and obtain the non-point source pollution load of measured rainfall events based on the response relationship between the characteristic factors and non-point source pollution: In the formula, Load M It is an actual measurement M Non-point source pollution load under each rainfall event; X M It is an actual measurement M Characteristic factor group of each rainfall event.

2. The method for estimating the non-point source pollution load from rainfall runoff according to claim 1, characterized in that, The target watershed basic data in step S1 includes topographic data, drainage system data, river cross-section data, measured rainfall runoff and water quality data; The topographic data, drainage system data, and river cross-section data are used in constructing the watershed non-point source pollution numerical model, while the measured rainfall, runoff, and water quality data are used in model parameter calibration and validation.

3. The method for estimating the non-point source pollution load from rainfall runoff according to claim 1, characterized in that, Step S2 defines non-point source pollution as rural domestic pollution, free-range livestock and poultry breeding pollution, agricultural non-point source pollution, and urban rainfall runoff pollution based on watershed characteristics; it divides the simulation period into fertilization period and non-fertilization period; and it classifies land use types into cultivated land, orchards, forest and grassland, construction land, and other land.

4. A system for estimating the non-point source pollution load from rainfall runoff, characterized in that, The system includes the following modules: Module M1: Obtain basic data for the target watershed; Module M2: Defines non-point source pollution, classifies time periods, and classifies land use types based on the characteristics of the target watershed; Module M3: Constructs an ICM integrated watershed model based on collected basic data, and calibrates the model using measured rainfall, runoff, and water quality data; Module M4: Calculates different rainfall characteristics and designs rainstorms based on historical rainfall data or rainstorm intensity formulas, and calculates and designs rainstorm processes based on typical rainstorms or design rainfall patterns; Module M5: Design characteristic factor groups for heavy rain and pre-rain drought periods based on different rainfall characteristics; Module M6: Based on the designed storm-driven model, obtain the area source pollution load of different characteristic factors; Module M7: Constructing the response relationship between non-point source pollution load and different characteristic factors; Module M8: Identifies the characteristic factors of measured rainfall events and obtains the non-point source pollution load of measured rainfall events based on the response relationship between the characteristic factors and the non-point source pollution load. The rainfall characteristics in module M4 include rainfall magnitude, rainfall pattern, and rainfall duration, wherein rainfall magnitude is expressed by return period, and rainfall pattern is expressed by peak rainfall coefficient. The ICM model in step S3 expresses the non-point source pollution formation process as a pollutant accumulation process, a pollutant flushing process, and a pollutant cleaning process. The pollutant accumulation model is any one of the following: Power function: Exponential function: Saturation function: In the formula, C 1 is the maximum possible cumulative amount; C 2 is the cumulative rate constant; C 3 represents the time index; C 4 is the half-saturation constant; t This refers to the number of days in the early drought period; The pollutant flushing model is any one of the following: Exponential equation: Flow characteristics scour curve: In the formula, C 5 is the scouring coefficient; C 6 is the scouring index; q It is the runoff per unit area; B It is accumulated pollutants; Q It is the runoff rate in the user-defined flow unit; The characteristic factor group includes rainfall duration, peak rainfall coefficient, return period, and pre-rain drought period; In the formula, X i It is the first i A set of characteristic factors; t i It is the first i The duration of the rainfall; r i It is the first i Rainfall peak coefficient; T i It is the first i One rainfall recurrence period; A i It is the first i A dry period before rain; Based on the designed storm-driven model, the non-point source pollution load under different characteristic factors is obtained. The non-point source pollution load of the target watershed is the sum of the non-point source pollution loads of each sub-watershed / sub-catchment: In the formula, Load T It is the total non-point source pollution load of the watershed; Load i,j It is the first i The characteristic factor group, the ... j Non-point source pollution load in individual watersheds / catchments; The response relationship between non-point source pollution load and different characteristic factors was constructed, and the response relationship between non-point source pollution load and characteristic factors was determined by methods such as multiple regression model and artificial neural network. In the formula, Load i,T1 It is the first i Non-point source pollution load during fertilization period under a group of characteristic factors; Load i,T2 It is the first i Non-point source pollution load during the non-fertilization period under a group of characteristic factors; Identify the characteristic factors of measured rainfall events, and obtain the non-point source pollution load of measured rainfall events based on the response relationship between the characteristic factors and non-point source pollution: In the formula, Load M It is an actual measurement M Non-point source pollution load under each rainfall event; X M It is an actual measurement M Characteristic factor group of each rainfall event.

5. The system for estimating the non-point source pollution load from rainfall runoff according to claim 4, characterized in that, The target watershed basic data in module M1 includes topographic data, drainage system data, river cross-section data, measured rainfall runoff and water quality data; The topographic data, drainage system data, and river cross-section data are used in constructing the watershed non-point source pollution numerical model, while the measured rainfall, runoff, and water quality data are used in model parameter calibration and validation.

6. The system for estimating the non-point source pollution load from rainfall runoff according to claim 4, characterized in that, Based on watershed characteristics, module M2 defines non-point source pollution as rural domestic pollution, free-range livestock and poultry breeding pollution, agricultural non-point source pollution, and urban rainfall runoff pollution; it divides the simulation period into fertilization period and non-fertilization period; and it classifies land use types into cultivated land, orchards, forest and grassland, construction land, and other land.

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