Method for constructing drainage basin scale phosphorus two-stage output coefficient model

Abstract

The invention relates to the field of water resource analysis models, discloses a drainage basin scale phosphorus two-stage output coefficient model construction method, and aims to accurately calculate drainage basin phosphorus output load. The method comprises the following steps: firstly, obtaining watershed related data including a digital elevation model (DEM), a river network system, phosphorus load, soil, meteorology, hydrology and total phosphorus concentration data of river water; secondly, calculating a sub-basin phosphorus output coefficient SPOC and a river channel phosphorus output coefficient RPOC, and evaluating phosphorus load transmission and attenuation conditions; then, constructing a regression model, and improving the accuracy of the model through regression analysis of factors such as rainfall, terrain gradient and river network density; then, calculating a basin total phosphorus output load based on a regression model, and analyzing a spatial-temporal change trend of the basin total phosphorus output load; finally, the model is verified and optimized through measured data, model parameters are adjusted through regression analysis and cross validation, and the prediction reliability is improved. The method can effectively evaluate the phosphorus output load change of the drainage basin, and provides support for water quality management.

Description

Technical Field

[0001] The present invention relates to the field of water resources analysis models, and in particular to a method for evaluating and modeling phosphorus output load at a watershed scale. Specifically, the present invention relates to a method for constructing a two-level phosphorus output coefficient model based on a watershed scale, which is used to simulate and predict the migration and output process of phosphorus in a watershed, and provide a scientific basis for watershed water quality management, pollution control and environmental protection. Background Art

[0002] Phosphorus is an indispensable nutrient element for aquatic ecosystems, but excessive phosphorus input can lead to eutrophication of water bodies, causing a series of ecological and environmental problems such as algae blooms, water quality deterioration and biodiversity decline. The sources of phosphorus pollution are complex, including point sources (such as sewage treatment plants and industrial emissions) and non-point sources (such as agriculture, livestock and poultry breeding, aquaculture, urban domestic sewage, etc.). With the increase in the intensity of point source pollution control, non-point source pollution has become the focus of water pollution prevention and control. The migration and transformation process of phosphorus is affected by factors such as rainfall runoff, underlying surface characteristics and human activities. It is highly complex, random and spatiotemporal, which brings challenges to management and governance.

[0003] Administrative departments conduct pollution source surveys based on administrative divisions, while river water quality monitoring uses natural watersheds as units. The two scopes do not match, resulting in inconsistent survey and assessment results. How to accurately and efficiently estimate phosphorus output loads based on administrative district phosphorus source survey data and clarify the contribution of each sub-basin has become the key to downstream water pollution prevention and control.

[0004] At present, the calculation methods of phosphorus output load mainly include monitoring method, empirical model and mechanism model. The monitoring method has high data accuracy, but it is difficult to obtain long-term and continuous data, and the workload is large, time-consuming, and costly, and the operability scale is limited. The empirical model is simple to calculate and has low data requirements, but it ignores the phosphorus migration and transformation process and has limited accuracy. The mechanism model has high accuracy, but the parameter setting is complex and requires a large amount of continuous measured data for calibration and verification.

[0005] In view of this, accurately and quickly evaluating the output load of phosphorus from different sources and identifying key phosphorus source areas and key influencing factors are technical challenges that urgently need to be solved in the current field of river basin water environment management. Summary of the invention

[0006] In order to achieve the above-mentioned invention object, the present invention provides the following technical solution: a method for constructing a two-level output coefficient model of phosphorus at a watershed scale, comprising the following steps: Step 1: Obtain relevant data of the target watershed, including digital elevation model (DEM), river network data, phosphorus load data, soil data, meteorological data, hydrological data, and river water total phosphorus concentration data; Step 2: Calculate the sub-basin phosphorus export coefficient SPOC and the river phosphorus export coefficient RPOC to evaluate the transmission and attenuation of phosphorus load at the sub-basin outlet and river, respectively; Step 3: Construct a two-level phosphorus output coefficient regression model, and establish a universal phosphorus output estimation model based on rainfall, terrain slope, river network density and river distance; Step 4: Calculate the total phosphorus output load at the basin outlet using the two-level phosphorus output coefficient model, and analyze the temporal and spatial variation trend of the phosphorus output load; Step 5: Adjust the model parameters based on the measured data to verify and optimize the constructed model.

[0007] Preferably, the step 1 of obtaining relevant data of the target watershed specifically includes: Obtain the terrain information of the basin based on the digital elevation model DEM and calculate the terrain slope and slope aspect; Use GIS technology to analyze river network data and extract the length of the river from the sub-basin outlet to the total outlet of the basin and river network density ; Collecting phosphorus loading data , the phosphorus loading data Including agricultural non-point source pollution, point source pollution and soil phosphorus reserves, among which the point source phosphorus load includes the phosphorus content and discharge volume in wastewater discharged from sewage treatment plants and industries, and the non-point source phosphorus load includes the phosphorus emission coefficient, area, output and population data in agricultural planting, livestock and poultry breeding, aquaculture and domestic sewage; soil data, including soil total phosphorus content, tillage layer depth, density and soil mobile phosphorus load ; Get weather data, including rainfall , temperature, and evaporation are used to simulate the phosphorus migration process; Collect hydrological data, including sub-basin outlet flow and the total outlet flow of the basin , and combined with the total phosphorus concentration data of river water, provide basic data for phosphorus output calculation. The total phosphorus concentration data of river water includes the total phosphorus concentration at the outlet of the sub-basin and total phosphorus concentration at the total outlet of the basin .

[0008] Preferably, the calculation of the sub-basin phosphorus output coefficient SPOC and the river phosphorus output coefficient RPOC in step 2 specifically includes: (1) The calculation of the sub-basin phosphorus export coefficient (SPOC) is based on the ratio of the sub-basin outlet phosphorus load to its source phosphorus load and the soil transferable phosphorus load. The calculation formula is as follows: ;in, For the Year The phosphorus export coefficient of each sub-basin ranges from [0,1], If it is less than 0, it is 0; if it is greater than 1, it is 1; For the Year The phosphorus output load at the outlet of each sub-basin is calculated as follows: ; For the Year Sub-basin outlet flow, For the Year Total phosphorus concentration at the outlet of each sub-basin; For the Year Source phosphorus loading for each sub-basin; For the Year The soil transportable phosphorus load in each sub-basin.

[0009] (2) The calculation of the river phosphorus export coefficient RPOC is based on the river phosphorus reduction coefficient RPC, which represents the reduction rate of phosphorus load per kilometer during river transmission. The calculation formula is as follows: ; ;in, For the Annual river phosphorus reduction coefficient, For the Year Phosphorus export load at each sub-basin outlet; For the Year The phosphorus export coefficient of the sub-basin river is in the range of [0,1], If it is less than 0, it is 0; if it is greater than 1, it is 1; For the The annual total phosphorus output load of the basin is calculated as follows: ; in, For the Total annual basin outlet flow, For the annual total phosphorus concentration at the total watershed outlet; For the The length of the river from the outlet of a sub-basin to the main outlet of the basin.

[0010] (3) The phosphorus output load data is obtained through distributed process model simulation, taking the sub-basin as a unit to simulate the migration and output process of phosphorus in the sub-basin and river. Preferably, the phosphorus two-level output coefficient regression model is constructed in step three, specifically including: (1) Sub-basin phosphorus export coefficient regression model, combined with rainfall , terrain slope and river network density , the calculation formula is established through regression analysis: ; in, For the Year The rainfall in each sub-basin, For the The topographic slope of the sub-basin, For the The river network density of each sub-basin.

[0011] (2) River phosphorus export coefficient regression model, combined with rainfall , terrain slope and length of river channel in sub-basins , determine the phosphorus output behavior of the river, and the calculation formula is as follows: ; in, For the Annual river rainfall, For the The topographic slope of the river from the sub-basin to the total outlet of the basin, For the The distance from a sub-basin to the total river outlet of the basin.

[0012] (3) The regression model is used to predict the phosphorus output characteristics under different watershed conditions and improve the accuracy of phosphorus load estimation.

[0013] Preferably, the step 4 of calculating the total outlet phosphorus output load of the basin according to the phosphorus two-stage output coefficient model specifically includes: (1) Based on the constructed regression model, the historical hydrological, meteorological and land use data of the study basin are selected to determine the number of sub-basins in the basin. , and collect the source phosphorus load of each sub-basin Soil transportable phosphorus load , using the regression model obtained in step S2 to calculate the sub-basin phosphorus export coefficient and river phosphorus export coefficient Calculate the total export phosphorus load from a watershed in a specific year using the following formula: ; in, is the number of sub-basins.

[0014] (2) Analyze the dynamic trend of phosphorus output load in different years; (3) Evaluate the contribution of specific sources to the total phosphorus output load and study their spatial distribution characteristics.

[0015] Preferably, the step five specifically includes: (1) The constructed model is verified using measured data, and the error between the model calculation value and the measured value is compared. The calculation formula is as follows: ; in, is the number of observations, is the measured data, Calculate values ​​for the model; (2) Adjust model parameters based on regression analysis to improve the applicability and accuracy of the model; (3) The cross-validation method is used to evaluate the stability of the model and optimize the weights of key influencing factors to improve the reliability of model prediction.

[0016] Compared with the prior art, the present invention has the following beneficial effects: Comprehensively consider the two-level process of phosphorus migration in the basin to improve the accuracy of phosphorus output simulation: by calculating the sub-basin phosphorus output coefficient SPOC and the river phosphorus output coefficient RPOC respectively, the present invention refines the migration and attenuation process of phosphorus load at different scales. Compared with the traditional single output coefficient model, it more accurately depicts the loss path and influencing factors of phosphorus load, thereby improving the prediction accuracy of basin phosphorus output. Based on multi-source data fusion, improve the applicability and universality of the model: using DEM data, GIS-analyzed river network data, soil phosphorus reserves, rainfall, flow, total phosphorus concentration and other multi-source data, combined with rainfall, terrain slope, river network density and river distance and other key factors to construct a regression model, so that the present invention can be applied to different types of basins, enhancing the universality and applicability of the model.

[0017] Introduce data-driven regression analysis to optimize the phosphorus output estimation method: A phosphorus output coefficient prediction model is constructed through regression analysis, so that the phosphorus migration process in sub-basins and rivers can be quantitatively estimated through key environmental factors. Compared with traditional empirical formulas, the robustness of the model is improved, while reducing dependence on large-scale measured data and improving calculation efficiency.

[0018] Establish a distributed simulation framework to enhance the ability to analyze the spatiotemporal changes of phosphorus load: adopt a distributed simulation method with sub-basins as the basic unit, so that the calculation of phosphorus output load can reflect the changing characteristics in different regions and time scales, support refined management and regulation, and provide accurate decision-making basis for phosphorus pollution control at the basin scale.

[0019] Combined with hydrological process simulation, the calculation accuracy of phosphorus load is optimized: by introducing hydrological parameters such as sub-basin outlet flow, total basin outlet flow, and river phosphorus reduction coefficient RPC, the present invention can dynamically simulate the transmission process of phosphorus in the hydrological cycle, overcoming the problem of simplified processing of phosphorus load transmission process in traditional methods and improving the adaptability to different hydrological scenarios.

[0020] Support phosphorus pollution control and watershed management decision-making, and provide scientific support for environmental protection: By simulating the spatiotemporal variation trend of phosphorus load, the present invention can be used to evaluate the contribution of different pollution sources (agriculture, industry, and domestic sewage) to the phosphorus output of the watershed, and then provide a scientific basis to optimize phosphorus pollution control strategies, support precise policy implementation, and help protect and restore the water ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the method steps provided for this application; Figure 2 Flowchart of model construction provided for this application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] refer to Figure 1 and Figure 2 The embodiment of the present invention provides a method for constructing a basin-scale phosphorus two-level output coefficient model, comprising the following steps: Step 1: Obtain relevant data of the target watershed, including digital elevation model (DEM), river network data, phosphorus load data, soil data, meteorological data, hydrological data, and river water total phosphorus concentration data.

[0024] In step one, the main function of step one is to obtain relevant data of the target watershed to provide basic support for the calculation of the phosphorus output coefficient. First, based on the digital elevation model DEM data, the terrain information of the watershed is extracted and the terrain slope is calculated. The GIS technology was used to analyze the river network data and extract the length of the river from the sub-basin outlet to the total outlet of the basin. and river network density , used to characterize the river's carrying capacity for phosphorus transport. At the same time, phosphorus loading data were collected , including agricultural non-point source pollution, point source pollution and soil phosphorus reserves. Among them, point source phosphorus load mainly comes from sewage treatment plants and industrial wastewater discharge, and its phosphorus content and emission data can be obtained from relevant environmental monitoring stations; non-point source phosphorus load includes phosphorus emission coefficients, areas, production and population data from sources such as agricultural planting, livestock and poultry breeding, aquaculture and domestic sewage, in order to characterize the contribution of different sources to phosphorus load. In addition, in order to further improve the accuracy of the model, soil data were obtained in the embodiment, including soil total phosphorus content, tillage layer depth, density and soil mobile phosphorus load , to assess the storage and mobility of phosphorus in the soil. At the same time, meteorological data such as rainfall are collected , temperature and evaporation to simulate the impact of rainfall runoff on phosphorus transport. The collection of hydrological data includes the outlet flow of the sub-basin and the total outlet flow of the basin , combined with river water total phosphorus concentration data, including total phosphorus concentration at the sub-basin outlet and total phosphorus concentration at the total outlet of the basin , ensuring the accuracy of phosphorus output calculations.

[0025] Step 2: Calculate the sub-basin phosphorus export coefficient SPOC and the river channel phosphorus export coefficient RPOC to evaluate the transmission and attenuation of phosphorus load at the sub-basin outlet and river channel respectively.

[0026] In step 2, first, the sub-basin phosphorus export coefficient SPOC is calculated. SPOC is used to characterize the ratio of the phosphorus load at the outlet of the sub-basin to the phosphorus load at the source and the transferable phosphorus load in the soil. The calculation formula is as follows: ; in, Indicates Year The phosphorus export coefficient of each sub-basin ranges from [0,1], If it is less than 0, it is taken as 0; if it is greater than 1, it is taken as 1. Sub-basin outlet phosphorus output load Flow at the outlet of the subbasin Total phosphorus concentration Multiplying together gives: ; in, is measured by a hydrological monitoring station and represents the discharge at the outlet of the sub-basin, while The total phosphorus concentration at the outlet of the sub-basin is obtained from water sample analysis. In addition, the source phosphorus load of the sub-basin Including agricultural non-point source pollution, industrial discharge and domestic sewage phosphorus content, while soil can transfer phosphorus load It is calculated from soil phosphorus storage data, tillage layer depth and soil density. These data are obtained through long-term monitoring and database analysis to provide input parameters for the model. Secondly, the river phosphorus export coefficient RPOC is calculated. RPOC is used to quantify the loss of phosphorus due to sedimentation, adsorption and degradation during river transport. Its calculation is based on the river phosphorus reduction coefficient RPC, that is, the average reduction rate of phosphorus load during the transmission process of each kilometer of river. The calculation formula is as follows: ; ; in, For the The annual river phosphorus reduction coefficient represents the phosphorus attenuation degree per unit distance; For the Year The phosphorus export coefficient of the river channel of each sub-basin is limited to [0,1]. Total outlet flow through the basin and total phosphorus concentration The calculation results are: ; in, Obtained from the flow monitoring data at the basin outlet, Measured by water quality testing equipment. Indicates The length of the river from the outlet of each sub-basin to the total outlet of the basin is obtained by GIS river network analysis and is used to calculate the transmission loss of phosphorus in the water system.

[0027] Finally, the phosphorus output load data was obtained by simulation using a distributed process model. The model uses sub-basins as calculation units to simulate the migration and output process of phosphorus in the basin. First, the scouring and transport of phosphorus by rainfall runoff are simulated by combining hydrological data, land use information and meteorological data; secondly, the retention and reduction of phosphorus in the river channel are simulated using deposition, diffusion and adsorption models. The simulation results of the model are calibrated with measured data to ensure the accuracy and applicability of the calculation.

[0028] Step 3: Construct a two-level phosphorus output coefficient regression model and establish a universal phosphorus output estimation model based on rainfall, terrain slope, river network density and river distance.

[0029] In this example, a sub-basin phosphorus output coefficient regression model is first constructed. The model is based on the rainfall in the sub-basin. , terrain slope and river network density Specifically, multiple sub-basins in the study area were selected, rainfall data for many years were collected, and the GIS was used to calculate the rainfall. GIS Extract slope and river network density parameters. Use regression analysis to establish the calculation formula for the sub-basin phosphorus output coefficient. , and a significance test was performed to screen out the variables that significantly affect phosphorus output, and finally determine the optimal regression equation. In addition, in order to verify the accuracy of the model, the cross-validation method was used to calculate the mean square error (MSE) and the coefficient of determination (CDR). , ensuring the reliability of the model.

[0030] Then, a river phosphorus output coefficient regression model was constructed, which combined rainfall , terrain slope and length of river Calculations were performed to determine the output behavior of phosphorus in the river. The specific steps included collecting rainfall data for different river sections in the study basin, using GIS technology to calculate the average slope from the sub-basin outlet to the total outlet of the basin, and determining the length of the river and the phosphorus output along the way through the hydrological model. Based on these data, the nonlinear regression method was used to fit the calculation formula , and perform parameter optimization and significance test. Finally, the uncertainty of the model is evaluated through Monte Carlo simulation, and historical data is used for verification, and the prediction error of the existing empirical model is compared to improve the applicability and stability of the regression model.

[0031] Based on the above two regression models, this method is used to predict the characteristics of phosphorus output under different hydrological and meteorological conditions and improve the accuracy of phosphorus load estimation. In the application process, data collection and preprocessing are first carried out on the target basin, and then the sub-basin phosphorus output coefficient model is used to calculate the phosphorus output of each sub-basin. Then, the phosphorus transmission and attenuation are simulated in combination with the river channel phosphorus output coefficient model, and finally the phosphorus load estimation value of the total outlet of the basin is obtained. By comparing the prediction accuracy of the model with the observed data and conducting simulation analysis for different rainfall scenarios and land use changes, a scientific basis can be provided for basin phosphorus pollution control and water resources management.

[0032] Step 4: Use the two-level phosphorus output coefficient model to calculate the total outlet phosphorus output load of the basin and analyze the temporal and spatial variation trends of the phosphorus output load.

[0033] In step 4, the total export phosphorus load of the basin in a specific year is first calculated based on the constructed regression model. Specifically, the historical hydrological, meteorological and land use data of the study basin are selected to determine the number of sub-basins in the basin. , and collect the source phosphorus load of each sub-basin Soil transportable phosphorus load The sub-basin phosphorus export coefficient was calculated using the above regression model. and river phosphorus export coefficient , and substitute it into the calculation formula The total export phosphorus output load for that particular year was calculated. During the calculation process, multi-source data fusion technology was used to improve the integrity of the data, and the uncertainty of the calculation results was evaluated through error analysis methods. At the same time, in order to ensure the rationality of the calculation, monitoring data under different rainfall scenarios were selected for comparative verification to adjust the regression model parameters and improve the calculation accuracy.

[0034] Secondly, the dynamic trend of phosphorus output load in different years was analyzed to identify the temporal characteristics of phosphorus pollution. , and use time series analysis methods (such as moving average method or trend analysis) to evaluate the long-term change pattern of phosphorus output load. Furthermore, by comparing meteorological data with land use change data, the influence of rainfall intensity, land development and agricultural fertilization on phosphorus output load is explored, and whether the change trend in different years is related to specific hydrological events (such as extreme rainfall or drought) is analyzed. In addition, statistical analysis methods are used to calculate the interannual coefficient of variation, evaluate the volatility of phosphorus output load, and combine watershed management measures to study whether there is a control effect of human intervention on phosphorus load.

[0035] Finally, the contribution of specific sources to the total phosphorus output load was evaluated and their spatial distribution characteristics were studied. First, the study basin was divided into multiple sub-basins and the distribution of each sub-basin was compared. and The relative contribution rate of phosphorus load was calculated to identify the main phosphorus source areas. The spatial distribution map of phosphorus output load was drawn by combining GIS spatial analysis technology, and the spatial gradient change of phosphorus load in the basin was analyzed by Kriging interpolation and other methods. In addition, the contribution rate of phosphorus load from different sources such as agricultural land, domestic sewage, and industrial emissions was further analyzed, and the impact of land use change on phosphorus output was studied through sensitivity analysis.

[0036] Step 5: Adjust the model parameters based on the measured data to verify and optimize the constructed model.

[0037] In step 5, the constructed two-level phosphorus output coefficient model is first verified using measured data, and the error between the model calculation value and the measured value is calculated. Specifically, hydrological monitoring data from different years in the study basin are selected, including the measured total phosphorus output load of the basin. and compare it with the phosphorus output load calculated by the model To quantitatively evaluate the model error, the root mean square error is calculated. : ; in, is the number of observations, For the The measured phosphorus load value at each observation time point is is the calculated value of the model. The prediction error of the model is evaluated through the root mean square error calculation results, and a scatter plot is drawn to intuitively analyze the deviation between the measured value and the calculated value. In addition, to further verify the applicability of the model, error analysis is performed on different hydrological years (flood year, normal water year, and dry year) to examine the adaptability and stability of the model under different hydrological conditions.

[0038] Secondly, the model parameters were adjusted based on regression analysis to improve the applicability and accuracy of the model. First, the model input parameters (such as rainfall) , terrain slope , river network density To conduct sensitivity analysis (such as least squares method, multivariate regression analysis, etc.), identify key variables that have a significant impact on model output, and adjust model parameters based on statistical regression methods (such as least squares method, multivariate regression analysis, etc.). and river phosphorus export coefficient regression model Parameter optimization is performed to minimize the mean square error between the calculated results and the measured values. In addition, optimization methods such as grid search and gradient descent are used to iteratively adjust the model parameters to improve the applicability of the model under different watershed conditions. The optimized model will be used to calculate phosphorus load again and compared with the measured data to ensure that the adjusted model can more accurately reflect the actual situation of phosphorus output.

[0039] Finally, the cross-validation method is used to evaluate the stability of the model and optimize the weights of key influencing factors to improve the reliability of model prediction. The specific steps of cross-validation are as follows: First, the measured data set is divided into a training set and a validation set, and the K-fold cross-validation method (such as K=5 or K=10) is used to train and validate the model respectively; second, in each iteration, the weights of the key influencing factors of the model (such as the regression coefficients of variables such as rainfall, terrain slope, and river network density) are adjusted to identify the optimal parameter combination; finally, the root mean square error of different validation sets is calculated. , mean square error (MSE), and other statistical indicators were used to draw error distribution diagrams to evaluate the stability and generalization ability of the model on different data sets. In addition, the Monte Carlo simulation method was used to analyze the uncertainty of the model, evaluate the impact of the fluctuation of input parameters on the output phosphorus load prediction results, and optimize the weight distribution strategy of the model to ensure that it can maintain high prediction accuracy and stability under various environmental conditions.

[0040] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0041] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A method for constructing a two-level output coefficient model of phosphorus at a watershed scale, characterized in that: The following steps are involved: S1. Obtain relevant data of the target watershed, including digital elevation model (DEM), river network data, phosphorus load data, soil data, meteorological data, hydrological data and river water total phosphorus concentration data; S2. Calculate the sub-basin phosphorus export coefficient SPOC and the river phosphorus export coefficient RPOC to evaluate the transmission and attenuation of phosphorus load at the sub-basin outlet and river, respectively; S3. Construct a two-level phosphorus output coefficient regression model and establish a universal phosphorus output estimation model based on rainfall, terrain slope, river network density and river distance; S4. Use the two-level phosphorus output coefficient model to calculate the total phosphorus output load of the basin and analyze the temporal and spatial variation trend of the phosphorus output load; S5. Adjust model parameters based on measured data to verify and optimize the constructed model.

2. The method for constructing a basin-scale two-level phosphorus output coefficient model according to claim 1, characterized in that: The step S1 of obtaining the relevant data of the target watershed specifically includes: Obtain the terrain information of the basin based on the digital elevation model DEM and calculate the terrain slope and slope aspect; Use GIS technology to analyze river network data and extract the length of the river from the sub-basin outlet to the total outlet of the basin and river network density ; Collecting phosphorus loading data , the phosphorus loading data Including agricultural non-point source pollution, point source pollution and soil phosphorus reserves, wherein the point source phosphorus load includes the phosphorus content and discharge amount in wastewater discharged by sewage treatment plants and industries, and the non-point source phosphorus load includes the phosphorus emission coefficient, area, output and population data in agricultural planting, livestock and poultry breeding, aquaculture and domestic sewage; Soil data, including soil total phosphorus content, tillage layer depth, density, and soil mobile phosphorus load ; Get weather data, including rainfall , temperature, and evaporation are used to simulate the phosphorus migration process; Collect hydrological data, including sub-basin outlet flow and the total outlet flow of the basin , and combined with the total phosphorus concentration data of river water, provide basic data for phosphorus output calculation. The total phosphorus concentration data of river water includes the total phosphorus concentration at the outlet of the sub-basin and total phosphorus concentration at the total outlet of the basin .

3. The method for constructing a basin-scale two-level phosphorus output coefficient model according to claim 1, characterized in that: The calculation of the sub-basin phosphorus output coefficient SPOC and the river phosphorus output coefficient RPOC in step S2 specifically includes: (1) The calculation of the sub-basin phosphorus export coefficient (SPOC) is based on the ratio of the sub-basin outlet phosphorus load to its source phosphorus load and the soil transferable phosphorus load. The calculation formula is as follows: ； in, For the Year The phosphorus export coefficient of each sub-basin ranges from [0,1], If it is less than 0, it is 0; if it is greater than 1, it is 1; For the Year The phosphorus output load at the outlet of each sub-basin is calculated as follows: ； For the Year Sub-basin outlet flow, For the Year Total phosphorus concentration at the outlet of each sub-basin; For the Year Source phosphorus loading for each sub-basin; For the Year Transportable phosphorus load in soil of each sub-basin; (2) The calculation of the river phosphorus export coefficient RPOC is based on the river phosphorus reduction coefficient RPC, which represents the reduction rate of phosphorus load per kilometer during river transmission. The calculation formula is as follows: ； ； in, For the Annual river phosphorus reduction coefficient, For the Year Phosphorus export load at each sub-basin outlet; For the Year The phosphorus export coefficient of the sub-basin river is in the range of [0,1], If it is less than 0, it is 0; if it is greater than 1, it is 1; For the The annual total phosphorus output load of the basin is calculated as follows: ； in, For the Total annual basin outlet flow, For the annual total phosphorus concentration at the total watershed outlet; For the The length of the river from the outlet of each sub-basin to the main outlet of the basin; (3) The phosphorus output load data were obtained through distributed process model simulation. Taking the sub-basin as a unit, the migration and output process of phosphorus in the sub-basin and river channel were simulated.

4. The method for constructing a basin-scale two-level phosphorus output coefficient model according to claim 1, characterized in that: The step S3 constructs a phosphorus two-level output coefficient regression model, which specifically includes: (1) Sub-basin phosphorus export coefficient regression model, combined with rainfall , terrain slope and river network density , the calculation formula is established through regression analysis: ； in, For the Year The rainfall in each sub-basin, For the The topographic slope of the sub-basin, For the River network density in each sub-basin; (2) River phosphorus export coefficient regression model, combined with rainfall , terrain slope and length of river channel in sub-basins , determine the phosphorus output behavior of the river, and the calculation formula is as follows: ； in, For the Annual river rainfall, For the The topographic slope of the river from the sub-basin to the total outlet of the basin, For the The distance from each sub-basin to the total outlet of the basin; (3) The regression model is used to predict the phosphorus output characteristics under different watershed conditions and improve the accuracy of phosphorus load estimation.

5. The method for constructing a basin-scale two-level phosphorus output coefficient model according to claim 1, characterized in that: In step S4, calculating the total outlet phosphorus output load of the basin according to the phosphorus two-stage output coefficient model specifically includes: (1) Based on the constructed regression model, the historical hydrological, meteorological and land use data of the study basin are selected to determine the number of sub-basins in the basin. , and collect the source phosphorus load of each sub-basin Soil transportable phosphorus load , using the regression model obtained in step S2 to calculate the sub-basin phosphorus export coefficient and river phosphorus export coefficient Calculate the total export phosphorus load from a watershed in a specific year using the following formula: ； in, is the number of sub-basins; (2) Analyze the dynamic trend of phosphorus output load in different years; (3) Evaluate the contribution of specific sources to the total phosphorus output load and study their spatial distribution characteristics.

6. The method for constructing a basin-scale two-level phosphorus output coefficient model according to claim 1, characterized in that: The step S5 specifically includes: (1) The constructed model is verified using measured data, and the error between the model calculation value and the measured value is compared. The calculation formula is as follows: ； in, is the number of observations, is the measured data, Calculate values ​​for the model; (2) Adjust model parameters based on regression analysis to improve the applicability and accuracy of the model; (3) The cross-validation method is used to evaluate the stability of the model and optimize the weights of key influencing factors to improve the reliability of model prediction.

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