Rainfall runoff and nitrogen and phosphorus flux dynamic estimation method, product, medium and equipment
The method uses the instantaneous unit line Nash model for nonlinear fitting to estimate rainfall runoff and nutrient fluxes, addressing scale-related inaccuracies and data complexity, enhancing prediction accuracy and practicality.
Patent Information
- Application Number
- CN202411209123.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-30
- Publication Date
- 2024-12-27
AI Technical Summary
Existing methods for estimating nitrogen and phosphorus fluxes in watersheds are limited by their inability to account for runoff differences at different rainfall scales, leading to inaccurate and impractical predictions, especially during extreme events, and require extensive data and complex parameter calibration.
A method utilizing the instantaneous unit line Nash model equation for nonlinear fitting to derive flow rise and fall curve equations, combined with nitrogen and phosphorus content data, to dynamically estimate rainfall runoff and fluxes, considering the correlation between rainfall and nutrient migration.
The method provides accurate and practical dynamic estimation of nitrogen and phosphorus fluxes, applicable across varying rainfall scales, reducing the need for extensive data and simplifying parameter requirements, thus improving prediction accuracy and applicability.
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Figure CN119207621A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of ecohydrological forecasting, and in particular to a method, product, medium and equipment for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes. Background Art
[0002] Nitrogen and phosphorus flux refers to the amount of specific nitrogen and phosphorus nutrients passing through a river section within a certain period of time. It can be divided into instantaneous flux and period flux. Water pollution caused by nutrient pollutants (total nitrogen, total phosphorus and nitrate) input into rivers due to agricultural activities is one of the most serious problems facing water resources protection and society in today's river basins. The occurrence of rainfall promotes the transfer of nutrient pollutants (such as nitrate) from the agricultural environment to the aquatic environment, resulting in important non-point source pollution problems. Therefore, quantitative and dynamic estimation of the dynamic process of nitrogen and phosphorus flux in the basin has become an important measure for scientific management and control of the basin environment.
[0003] Scholars at home and abroad have proposed methods such as the average concentration method, rainfall difference method, and output coefficient method to make simple estimates of nitrogen and phosphorus fluxes in watersheds, but these methods still have many limitations. With the increase in extreme rainfall events, the dynamic changes in nitrogen and phosphorus fluxes in watersheds have also shown significant fluctuations. The series of estimation formulas in the above-mentioned existing methods ignore the impact of runoff differences at different rainfall scales on the migration of nitrogen and phosphorus fluxes, which limits their application at the sub-rainstorm scale, or do not involve the dynamic changes in nitrogen and phosphorus fluxes in a single rainfall event, and the estimation accuracy is not high. In addition, the existing non-point source pollution load prediction models at home and abroad have high data requirements, many parameters, and are difficult to calibrate, so they are not very practical and difficult to promote. Summary of the invention
[0004] The purpose of this application is to provide a method, product, medium and equipment for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes, which can effectively improve the accuracy and practicality of dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes.
[0005] To achieve the above objectives, this application provides the following solutions:
[0006] In a first aspect, the present application provides a method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes, comprising:
[0007] Obtaining basic monitoring data of rainfall runoff; the basic monitoring data includes runoff data and rainfall data;
[0008] Different rainfall types are classified based on rainfall data; the rainfall types include light rain, moderate rain, heavy rain and rainstorm;
[0009] Collecting nitrogen and phosphorus content data of rainfall runoff under different rainfall types; the nitrogen and phosphorus content data include concentration and flux of each nitrogen and phosphorus component;
[0010] Based on the runoff data, the instantaneous unit line Nash model equation is used for nonlinear fitting to obtain the flow rise and fall curve equation.
[0011] Based on the nitrogen and phosphorus content data, the flow rate rise and fall curve equation was used for nonlinear fitting to obtain the nitrogen and phosphorus flux dynamic curve equation;
[0012] The rainfall runoff and nitrogen and phosphorus fluxes were dynamically estimated based on the flow rise and fall curve equation and the nitrogen and phosphorus flux dynamic curve equation.
[0013] Optionally, the obtaining of basic monitoring data of rainfall runoff specifically includes:
[0014] Use flow monitoring equipment to obtain runoff data at the rainfall runoff outlet section;
[0015] The statistical data of rain gauges are obtained through the hydrological monitoring stations in the rainfall runoff area, and based on the point rainfall data in the statistical data of rain gauges, the surface rainfall data are calculated using the Thiessen polygon method as the rainfall data.
[0016] Optionally, the dividing of different rainfall types based on rainfall data specifically includes:
[0017] Based on the rainfall data for three consecutive days, when the rainfall data for three consecutive days is less than 10mm, the rainfall type is light rain; when the rainfall data for three consecutive days is greater than or equal to 10mm and less than 25mm, the rainfall type is moderate rain; when the rainfall data for three consecutive days is greater than or equal to 25mm and less than 50mm, the rainfall type is heavy rain; when the rainfall data for three consecutive days is greater than or equal to 50mm, the rainfall type is torrential rain, thus dividing different rainfall types.
[0018] Optionally, the collecting of nitrogen and phosphorus content data of rainfall runoff under different rainfall types specifically includes:
[0019] Collect nitrogen and phosphorus content data before, during and after rainfall under different rainfall types at the outlet section of the rainfall runoff monitoring basin.
[0020] Optionally, before the nonlinear fitting is performed based on the runoff data using the instantaneous unit line Nash model equation to obtain the flow rise and fall change curve equation, the method further includes:
[0021] The Pearson correlation coefficient method was used to quantitatively analyze the response of nitrogen and phosphorus content data to rainfall and runoff changes.
[0022] Optionally, the specific expression of the instantaneous unit line Nash model equation is: Among them, u(0,t) is the function of the instantaneous unit line; K is a parameter reflecting the regional runoff confluence and regulation; n is the number of regulation times; t is the time since the beginning of rainfall; Γ(·) is the gamma function.
[0023] Optionally, the specific expression of the flow rate rise and fall change curve equation is: Among them, A is the parameter of the flow rise and fall change curve equation; q0 is the 2-hour average of the rising flow and the flow after the fall during the rising and falling process of the rainfall runoff area; q is the flow at the rainfall runoff outlet section at time t.
[0024] In a second aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes.
[0025] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes.
[0026] In a fourth aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes.
[0027] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0028] The present application provides a method, product, medium and equipment for dynamic estimation of rainfall runoff and nitrogen and phosphorus flux, mainly involving a method for estimating rainfall runoff response and nitrogen and phosphorus flux dynamic process in agricultural irrigation areas by obtaining flow rise and fall change curve equation and nitrogen and phosphorus flux dynamic curve equation of rainfall runoff based on instantaneous unit line Nash model equation. Different from the traditional nitrogen and phosphorus flux estimation method, the rainfall runoff and nitrogen and phosphorus flux dynamic estimation method of the present application fully considers the correlation between rainfall and nitrogen and phosphorus flux in runoff, and is based on the instantaneous unit line Nash model equation. By obtaining the flow rise and fall change curve equation and nitrogen and phosphorus flux dynamic curve equation at the outlet section of the rainfall runoff basin, the dynamic process of basin runoff and nitrogen and phosphorus flux during the entire rainfall process is simulated, and a large amount of continuous nitrogen and phosphorus flux monitoring data is not required. It can be applied to rainfall of different scales and can estimate the dynamic change process of nitrogen and phosphorus flux during a single rainfall process. Therefore, the rainfall runoff and nitrogen-phosphorus flux dynamic estimation method, product, medium and equipment of the present application can effectively improve the accuracy and practicality of the rainfall runoff and nitrogen-phosphorus flux dynamic estimation. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0030] Figure 1 Flowchart of the dynamic estimation method of rainfall runoff and nitrogen and phosphorus fluxes provided for this application. DETAILED DESCRIPTION
[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0032] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.
[0033] like Figure 1 As shown, the present application discloses a method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes, comprising:
[0034] Step 1: Obtain basic monitoring data of rainfall runoff.
[0035] First, monitor the rainfall runoff area, and use flow monitoring equipment to obtain runoff data at the outlet section of the agricultural small watershed (rainfall runoff). Obtain rainfall monitoring data (rain gauge statistical data) through the hydrological monitoring station in the rainfall runoff area, and use the Thiessen polygon method to calculate the basin surface rainfall data (surface rainfall data) based on the point rainfall data monitored by the rain gauge. The runoff data and rainfall data are used as basic monitoring data.
[0036] Specifically, the rainfall data of the rainfall runoff area in the present application is calculated using the statistical data of the rain gauges of the hydrological monitoring stations set up in the watershed, and the Thiessen polygon method is mainly used to calculate the rainfall data. The Thiessen polygon method is also called the perpendicular bisect method or the weighted average method. The method is to first obtain the area weight coefficient of each rain gauge (hydrological monitoring station), and then multiply the rainfall of each station by the weight of the area occupied by the station and add them up to obtain the surface rainfall. The method is to connect the adjacent rain gauges in the watershed with straight lines, and make perpendicular bisectors of each connecting line. These bisectors intersect and divide the watershed into several polygons. There is a rain gauge in each polygon. Assume that each rain gauge uses the polygon where it is located as the control area a, and the ratio of a to the area S of the entire watershed f=a / S is the weight number of the rain gauge, thereby obtaining the average surface rainfall of the watershed. As rainfall data:
[0037]
[0038] Among them, f i P is the weight calculated by polygon area for each rainfall station; i is the rainfall at each rain gauge during the same period.
[0039] Step 2: Classify different rainfall types based on rainfall data.
[0040] According to the rainfall data and the actual rainfall situation in the rainfall runoff basin, the accumulated rainfall data for three consecutive days are analyzed according to the conditions of light rain, moderate rain, heavy rain and rainstorm. When the rainfall data for three consecutive days is less than 10mm, the rainfall type is light rain; when the rainfall data for three consecutive days is greater than or equal to 10mm and less than 25mm, the rainfall type is moderate rain; when the rainfall data for three consecutive days is greater than or equal to 25mm and less than 50mm, the rainfall type is heavy rain; when the rainfall data for three consecutive days is greater than or equal to 50mm, the rainfall type is rainstorm, thus different rainfall types are divided. For different rainfall types, there will be runoff processes of different scales. Subsequently, the corresponding flow rise and fall change curve equations are established for rainfall of different scales, and the flow rise and fall curves under different scales of rainfall intensities can be obtained, that is, a flood process line is obtained.
[0041] Step 3: Collect nitrogen and phosphorus content data of rainfall runoff under different rainfall types.
[0042] After different rainfall types are divided, the nitrogen and phosphorus content data of the rainfall runoff area under different rainfall types are monitored. Specifically, water samples are collected at the outlet section of the basin before, during and after rainfall to obtain the nitrogen and phosphorus content data of various forms in the water. The nitrogen and phosphorus content data include the concentration and flux of each nitrogen and phosphorus component.
[0043] At the same time, in order to more accurately and quantitatively analyze the response of nitrogen and phosphorus content data changes to rainfall runoff changes, this application uses the Pearson correlation coefficient method for correlation analysis. If the changes are consistent, the next step of equation construction is carried out. As a specific embodiment, due to agricultural activities in agricultural irrigation areas, the concentration and flux of each component of nitrogen and phosphorus are consistent with the rise and fall trend of runoff, and the correlation is large.
[0044] Specifically, the correlation coefficient is represented by r, and the calculation formula of the Pearson correlation coefficient method is:
[0045]
[0046] Among them, x and y are the corresponding values of two variables at the same time, the fixed variable x is the runoff flow, and the variable y is the concentration and flux of each component of nitrogen and phosphorus; and are the average values of the two variables.
[0047] According to the principles of hydrology, the changes in basin flow and runoff solutes have a hysteresis. Taking the actual lag time of the concentrations of each nitrogen and phosphorus component monitored in a specific rainfall event as a reference, the change curve of the concentrations of each nitrogen and phosphorus component is moved forward. Then, with rainfall as the fixed variable x and the concentrations of each nitrogen and phosphorus component as the variable y, the correlation coefficient is calculated and analyzed to obtain the response of the concentration changes of each nitrogen and phosphorus component to the changes in rainfall and runoff.
[0048] Step 4: Based on the runoff data, the instantaneous unit line Nash model equation is used for nonlinear fitting to obtain the flow rise and fall curve equation.
[0049] Based on the instantaneous unit line Nash model equation, the runoff data is nonlinearly fitted, and the parameters are iteratively adjusted to establish the flow rise and fall curve equation.
[0050] The instantaneous unit line Nash model equation is:
[0051]
[0052] Among them, K is a parameter reflecting the regional runoff confluence and regulation function of rainfall runoff; n is the number of regulation times; t is the time since the beginning of rainfall; Γ(·) is the gamma function; u(0,t) is the function of the instantaneous unit line. The surface flow process line formed at the outlet section by the unit net rainfall in the extremely small period of time in the basin (i.e., t tends to 0) is called the instantaneous unit line, and its vertical height is represented by u(0,t) or u(t). The ratio assumption and superposition assumption of the instantaneous unit line are the same as those of the empirical unit line.
[0053] The data of runoff rise and fall during rainfall runoff are selected to draw a scatter plot. Based on the instantaneous unit line Nash model equation, the runoff data is nonlinearly fitted, and the parameter K is iteratively adjusted to establish the flow rise and fall change curve equation. Because the flow rise and fall process in the rainfall runoff process is relative, it is not suitable to be expressed by a single nonlinear function. Here, data analysis is performed in the flow rise and fall stages to establish the change curve equation.
[0054] Based on formula (3), the process of runoff rise and fall in a flood can be described. Since the equation is in the form of an exponential function, the basic function of the equation of the flow rise and fall change curve of rainfall runoff can be set to the exponential function form:
[0055]
[0056] Among them, A is the parameter of the flow rise and fall variation curve equation; q0 is the 2-hour average of the rise and fall flow and the fall flow in the rainfall runoff area; q is the flow at the rainfall runoff outlet section at time t. The runoff data in a rainfall process is fitted with a nonlinear curve based on formula (4), and the parameters A and K are iteratively adjusted. Finally, the flow rise and fall variation curve equation of the rainfall runoff can be obtained.
[0057] Step 5: Based on the nitrogen and phosphorus content data, the flow rise and fall curve equation is used for nonlinear fitting to obtain the dynamic curve equation of nitrogen and phosphorus flux.
[0058] Based on the rainfall runoff flow rise and fall curve equation obtained in step 4, a curve equation is established for the concentration and flux of each nitrogen and phosphorus component by nonlinear fitting, the dynamic curve equation of nitrogen and phosphorus flux is obtained, and the fitting effect of the dynamic curve equation of nitrogen and phosphorus flux is evaluated.
[0059] After correlation identification, the basic equations of the rising and falling curves of the concentrations and fluxes of each component of nitrogen and phosphorus are based on the basic functions of the flow rise and fall curve equation in step 4, and the established functional relationship is as follows:
[0060]
[0061] Among them, A' is the parameter of the dynamic curve equation of nitrogen and phosphorus flux; F1 is the average value of the concentration and flux of nitrogen and phosphorus components during the rise and fall of the concentration and flux of nitrogen and phosphorus components and after the fall of the concentration and flux of nitrogen and phosphorus components; F is the flux of nitrogen and phosphorus components at the outlet section of the basin at time t. Based on formula (5) and the concentration and flux of nitrogen and phosphorus components measured during rainfall, the dynamic curve equation of nitrogen and phosphorus flux during rainfall runoff can be obtained by iteratively adjusting the parameters A' and K.
[0062] After fitting the equation, the fitting effects of the two stages of rainfall intensity rise and fall were calculated using the R 2 To evaluate, R 2 The closer it is to 1, the better the fitting effect is.
[0063] Step 6: Dynamically estimate rainfall runoff and nitrogen and phosphorus fluxes based on the flow rise and fall curve equation and the nitrogen and phosphorus flux dynamic curve equation.
[0064] Specifically, the rise and fall curve is obtained, which means the process of how the flow / nitrogen and phosphorus fluxes change with time in a complete rainfall event. The flow / flux corresponding to different times at the basin outlet section can be estimated, and the dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes can be completed.
[0065] Compared with the existing nitrogen and phosphorus flux estimation methods, the rainfall runoff and nitrogen and phosphorus flux dynamic estimation method of this application has the following advantages:
[0066] 1) Since rainfall is the main cause of non-point source pollution in agricultural watersheds, rainfall runoff is closely related to the dynamics of the concentrations and fluxes of various nitrogen and phosphorus components. The dynamic estimation method of rainfall runoff and nitrogen and phosphorus flux in this application obtains the changing process of the concentrations and fluxes of various nitrogen and phosphorus components in rainfall runoff based on the law of runoff changes during rainfall, which can more truly reflect the relationship between rainfall runoff and the migration of nitrogen and phosphorus pollution components.
[0067] 2) The rainfall runoff and nitrogen and phosphorus flux dynamic estimation method of this application can be applied to areas where hydrological data are scarce. It does not require long-term and high-frequency runoff water quality data, and the parameters are simple and easy to obtain.
[0068] In summary, the rainfall runoff and nitrogen-phosphorus flux dynamic estimation method of the present application obtains the rainfall runoff rise and fall curve equation for different rainfall scales based on the instantaneous unit line Nash model equation, and further obtains the dynamic curve equation of nitrogen-phosphorus flux in rainfall runoff. This can make the nitrogen-phosphorus pollution estimation method more accurate, unaffected by the rainfall scale, and can effectively estimate its dynamic change process. It emphasizes the role of rainfall on agricultural non-point source pollution, effectively reduces the error impact of different rainfall scales on the estimation results, and can estimate the pollution flux dynamics in a single rainfall. It provides guidance for pollution control and prediction in agricultural irrigation areas, sustainable development of agricultural activities, and the formulation of relevant policies, and has strong practical significance.
[0069] In some embodiments, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes.
[0070] In some embodiments, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes.
[0071] In some embodiments, the present application also provides a computer device, including a processor, a memory, an input / output interface (Input / Output, referred to as I / O), a communication interface, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the dynamic estimation method of rainfall runoff and nitrogen and phosphorus fluxes.
[0072] Wherein, the processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes is implemented.
[0073] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.
[0074] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.
[0075] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0076] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes, characterized in that: include: Obtain basic monitoring data on rainfall runoff; The basic monitoring data include runoff data and rainfall data; Different rainfall types are classified based on rainfall data; the rainfall types include light rain, moderate rain, heavy rain and rainstorm; Collecting nitrogen and phosphorus content data of rainfall runoff under different rainfall types; the nitrogen and phosphorus content data include concentration and flux of each nitrogen and phosphorus component; Based on the runoff data, the instantaneous unit line Nash model equation is used for nonlinear fitting to obtain the flow rise and fall curve equation. Based on the nitrogen and phosphorus content data, the flow rate rise and fall curve equation was used for nonlinear fitting to obtain the nitrogen and phosphorus flux dynamic curve equation; The rainfall runoff and nitrogen and phosphorus fluxes were dynamically estimated based on the flow rise and fall curve equation and the nitrogen and phosphorus flux dynamic curve equation.
2. The method for dynamic estimation of rainfall runoff and nitrogen and phosphorus flux according to claim 1, characterized in that: The basic monitoring data of rainfall runoff is obtained, specifically including: Use flow monitoring equipment to obtain runoff data at the rainfall runoff outlet section; The statistical data of rain gauges are obtained through the hydrological monitoring stations in the rainfall runoff area, and based on the point rainfall data in the statistical data of rain gauges, the surface rainfall data are calculated using the Thiessen polygon method as the rainfall data.
3. The method for dynamic estimation of rainfall runoff and nitrogen and phosphorus flux according to claim 1, characterized in that: The classification of different rainfall types based on rainfall data specifically includes: Based on the rainfall data for three consecutive days, when the rainfall data for three consecutive days is less than 10mm, the rainfall type is light rain; when the rainfall data for three consecutive days is greater than or equal to 10mm and less than 25mm, the rainfall type is moderate rain; when the rainfall data for three consecutive days is greater than or equal to 25mm and less than 50mm, the rainfall type is heavy rain; when the rainfall data for three consecutive days is greater than or equal to 50mm, the rainfall type is torrential rain, thus dividing different rainfall types.
4. The method for dynamic estimation of rainfall runoff and nitrogen and phosphorus flux according to claim 1, characterized in that: The collecting of nitrogen and phosphorus content data of rainfall runoff under different rainfall types specifically includes: Collect nitrogen and phosphorus content data before, during and after rainfall under different rainfall types at the outlet section of the rainfall runoff monitoring basin.
5. The method for dynamic estimation of rainfall runoff and nitrogen and phosphorus flux according to claim 1, characterized in that: Before the nonlinear fitting is performed based on the runoff data using the instantaneous unit line Nash model equation to obtain the flow rise and fall change curve equation, the method further includes: The Pearson correlation coefficient method was used to quantitatively analyze the response of nitrogen and phosphorus content data to rainfall and runoff changes.
6. The method for dynamic estimation of rainfall runoff and nitrogen and phosphorus flux according to claim 1, characterized in that: The specific expression of the instantaneous unit line Nash model equation is: Among them, u(0,t) is the function of the instantaneous unit line; K is a parameter reflecting the regional runoff confluence and regulation; n is the number of regulation times; t is the time since the beginning of rainfall; Γ(·) is the gamma function.
7. The method for dynamic estimation of rainfall runoff and nitrogen and phosphorus flux according to claim 6, characterized in that: The specific expression of the flow rate rise and fall change curve equation is: Among them, A is the parameter of the flow rise and fall change curve equation; q0 is the 2-hour average of the rising flow and the flow after the fall during the rising and falling process of the rainfall runoff area; q is the flow at the rainfall runoff outlet section at time t.
8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes described in any one of claims 1 to 7 is implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes described in any one of claims 1 to 7 is implemented.
10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for dynamic estimation of rainfall runoff and nitrogen and phosphorus fluxes according to any one of claims 1 to 7.