A method for constructing a two-level output coefficient model of phosphorus at the basin scale

Abstract

The present invention relates to the field of water resource analysis models, and discloses a method for constructing a two-level output coefficient model of phosphorus in a watershed scale, aiming to accurately calculate the phosphorus output load in the watershed. The method includes the following steps: First, obtain relevant data of the watershed, including digital elevation model DEM, river network system, phosphorus load, soil, meteorology, hydrology and total phosphorus concentration data of river water; Second, calculate the phosphorus output coefficient of the sub-watershed and the phosphorus output coefficient of the river channel, and evaluate the transmission and attenuation of phosphorus load; Then, construct a regression model, and improve the accuracy of the model through regression analysis of factors such as rainfall, terrain slope, and river network density; Next, calculate the total phosphorus output load of the watershed based on the regression model, and analyze its temporal and spatial variation trends; Finally, use the measured data to verify and optimize the model, and adjust the model parameters through regression analysis and cross-validation to improve the prediction reliability. The present invention can effectively evaluate the change of phosphorus output load in the watershed and provide support for water quality management.

Description

Technical Field

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

[0002] Phosphorus is an essential nutrient element in aquatic ecosystems, but excessive phosphorus input can lead to water eutrophication, causing a series of ecological and environmental problems such as cyanobacterial blooms, water quality deterioration and biodiversity decline. The sources of phosphorus pollution are complex, including point sources (such as sewage treatment plants, industrial emissions) and non-point sources (such as agriculture, livestock and poultry farming, aquaculture, urban domestic sewage, etc.). With the increasing 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, and has high complexity, randomness and spatio-temporal variability, which brings challenges to management and treatment.

[0003] Administrative departments conduct pollution source investigations based on administrative divisions, while river water quality monitoring is carried out in natural basins. The scopes of the two do not match, resulting in inconsistent investigation and evaluation results. How to accurately and efficiently estimate the phosphorus output load based on the phosphorus source investigation data of administrative regions and clarify the contributions of each sub-basin has become the key to water pollution prevention and control in the downstream water body.

[0004] At present, the calculation methods of phosphorus output load mainly include the monitoring method, empirical models and mechanism models. The monitoring method has high data accuracy, but it is difficult to obtain long-term and continuous data, and has a large workload, long time-consuming, high cost, and limited operable scale. Empirical models are simple to calculate and have low data requirements, but they ignore the phosphorus migration and transformation process and have limited accuracy. Mechanism models have high accuracy, but the parameter setting is complex and a large amount of continuous measured data is required for calibration and verification.

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

[0006] In order to achieve the above invention purposes, the present invention provides the following technical solutions: A method for constructing a two-level phosphorus output coefficient model at the basin scale, including the following steps:

[0007] Step 1: Obtain relevant data of the target basin, including digital elevation model DEM, river network water system data, phosphorus load data, soil data, meteorological data, hydrological data and total phosphorus concentration data of river water;

[0008] Step 2: Calculate the phosphorus output coefficients of sub-watersheds and the phosphorus output coefficients of river channels to evaluate the transport and attenuation of phosphorus loads at the outlets of sub-watersheds and in river channels respectively;

[0009] 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 channel distance;

[0010] Step 4: Use the two-level phosphorus output coefficient model to calculate the total phosphorus output load at the outlet of the watershed and analyze the temporal and spatial variation trends of the phosphorus output load;

[0011] Step 5: Validate and optimize the constructed model by adjusting the model parameters based on measured data.

[0012] Preferably, obtaining the relevant data of the target watershed in Step 1 specifically includes:

[0013] Obtain the terrain information of the watershed based on the Digital Elevation Model (DEM) and calculate the terrain slope and aspect;

[0014] Use GIS technology to analyze the river network water system data and extract the river channel length from the outlet of the sub-watershed to the total outlet of the watershed and river network density ;

[0015] Collect phosphorus load data , and the phosphorus load data includes agricultural non-point source pollution, point source pollution, and soil phosphorus reserves. Among them, the point source phosphorus load includes the phosphorus content and discharge in the wastewater discharged from sewage treatment plants and industries, and the non-point source phosphorus load includes the phosphorus discharge coefficients, areas, yields, and population data in agricultural planting, livestock and poultry breeding, aquaculture, and domestic sewage;

[0016] Soil data, including total soil phosphorus content, tillage layer depth, density, and soil mobile phosphorus load ;

[0017] Obtain meteorological data, including rainfall , temperature, and evaporation, for simulating the phosphorus migration process;

[0018] Collect hydrological data, and the hydrological data includes the flow at the outlet of the sub-watershed and the flow at the total outlet of the watershed , 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-watershed and the total phosphorus concentration at the total outlet of the watershed .

[0019] Preferably, in the second step, the phosphorus output coefficient of the sub-watershed and the phosphorus output coefficient of the river channel are calculated as follows:

[0020] (1) Calculation of the phosphorus output coefficient of the sub-watershed is based on the ratio of the phosphorus output load at the outlet of the sub-watershed to its source phosphorus load and the soil mobile phosphorus load. The calculation formula is as follows:

[0021] ;

[0022] where, is the phosphorus output coefficient of the th sub-watershed in the th year, ranging from [0, 1]. If it is less than 0, take 0; if it is greater than 1, take 1.

[0023] is the phosphorus output load at the outlet of the th sub-watershed in the

[0024] ;

[0025] is the outlet flow of the th sub-watershed in the th th year, is the total phosphorus concentration at the outlet of the

[0026] th th sub-watershed;

[0027] is the source phosphorus load of the th sub-watershed in the

[0028] (2) Calculation of the phosphorus output coefficient of the river channel is based on the phosphorus reduction coefficient of the river channel, which represents the reduction rate per kilometer of the phosphorus load during the river channel transmission process. The calculation formula is as follows:

[0029] ;

[0030] ;

[0031] where, is the phosphorus reduction coefficient of the river channel in the th year, is the th Phosphorus output load at the outlet of each sub - basin;

[0032] For the th year and the th sub - basin, the phosphorus output coefficient of the river channel ranges from [0, 1]. If it is less than 0, take 0; if it is greater than 1, take 1.

[0033] For the th year, the total phosphorus output load at the total outlet of the basin, and the calculation formula is:

[0034] ;

[0035] Among them, is the total outlet flow of the basin in the th year, is the total phosphorus concentration at the total outlet of the basin in the th year;

[0036] For the th sub - basin, it is the river channel length from the sub - basin outlet to the total outlet of the basin.

[0037] (3) The phosphorus output load data is obtained by simulating through a distributed process model. Taking the sub - basin as the unit, it simulates the migration and output process of phosphorus in the sub - basin and the river channel.

[0038] Preferably, in step three, constructing a two - level phosphorus output coefficient regression model specifically includes:

[0039] (1) Sub - basin phosphorus output coefficient regression model, combined with rainfall , terrain slope and river network density , establish a calculation formula through regression analysis:

[0040] ;

[0041] Among them, is the rainfall of the th year and the th sub - basin, is the terrain slope of the th sub - basin, is the river network density of the th sub - basin.

[0042] (2) River - channel phosphorus output coefficient regression model, combined with rainfall , terrain slope and the river channel length of the sub - basin , determine the phosphorus output behavior of the river channel, and the calculation formula is as follows:

[0043] ;

[0044] Among them, is the channel rainfall in the th year, is the topographic slope of the th sub - watershed to the main outlet of the watershed, is the distance from the th sub - watershed to the main outlet of the watershed.

[0045] (3) The regression model is used to predict the phosphorus output characteristics under different watershed conditions and improve the accuracy of phosphorus load estimation.

[0046] Preferably, calculating the total phosphorus output load at the main outlet of the watershed according to the two - stage phosphorus output coefficient model in step four specifically includes:

[0047] (1) Based on the constructed regression model, select the historical hydrological, meteorological and land - use data of the study watershed, determine the number of sub - watersheds in the watershed, and collect the source phosphorus load and soil mobile phosphorus load of each sub - watershed, and use the regression model obtained in step S2 to calculate the sub - watershed phosphorus output coefficient and the channel phosphorus output coefficient to calculate the total phosphorus output load at the main outlet of the watershed in a specific year. The calculation formula is as follows:

[0048] ;

[0049] Among them, is the number of sub - watersheds.

[0050] (2) Analyze the dynamic change trend of phosphorus output load in different years;

[0051] (3) Evaluate the contribution of specific sources to the total phosphorus output load and study its spatial distribution characteristics.

[0052] Preferably, step five specifically includes:

[0053] (1) Use the measured data to verify the constructed model and compare the error between the model calculated value and the measured value. The calculation formula is as follows:

[0054] ;

[0055] Among them, is the number of observed data, is the measured data, is the model calculated value;

[0056] (2) Adjust the model parameters based on regression analysis to improve the applicability and accuracy of the model;

[0057] (3) Use the cross-validation method to evaluate the stability of the model and optimize the weights of key influencing factors to enhance the reliability of model prediction.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] Comprehensively consider the two-stage process of phosphorus migration in the basin to improve the accuracy of phosphorus output simulation: By calculating the phosphorus output coefficient of the sub-basin and the phosphorus output coefficient of the river channel respectively, the present invention details the migration and attenuation process of phosphorus load at different scales, and more accurately depicts the loss path and influencing factors of phosphorus load compared with the traditional single output coefficient model, thereby improving the prediction accuracy of basin phosphorus output.

[0060] Based on multi-source data fusion, improve the applicability and universality of the model: Use multi-source data such as DEM data, river network data parsed by GIS, soil phosphorus reserves, rainfall, flow, total phosphorus concentration, etc., and construct a regression model in combination with key factors such as rainfall, terrain slope, river network density, and river channel distance, enabling the present invention to be applicable to different types of basins and enhancing the universality and applicability of the model.

[0061] Introduce data-driven regression analysis to optimize the phosphorus output estimation method: Construct a phosphorus output coefficient prediction model through regression analysis, enabling the phosphorus migration process in the sub-basin and river channel to be quantitatively estimated by key environmental factors. Compared with the traditional empirical formula, the robustness of the model is improved, and at the same time, the dependence on a large amount of measured data is reduced, and the calculation efficiency is enhanced.

[0062] Establish a distributed simulation framework to enhance the ability to analyze the spatio-temporal variation of phosphorus load: Adopt a distributed simulation method with sub-basins as the basic unit, enabling the calculation of phosphorus output load to reflect the variation characteristics at different regional and time scales, supporting refined management and regulation, and providing accurate decision-making basis for phosphorus pollution control at the basin scale.

[0063] Combine hydrological process simulation to optimize the calculation accuracy of phosphorus load: By introducing hydrological parameters such as the flow at the outlet of the sub-basin and the total flow at the outlet of the basin, as well as the phosphorus reduction coefficient of the river channel, the present invention can dynamically simulate the transmission process of phosphorus in the hydrological cycle, overcome the problem of simplified treatment of the phosphorus load transmission process in the traditional method, and enhance the adaptability to different hydrological scenarios.

[0064] Support phosphorus pollution control and watershed management decision-making, providing scientific support for environmental protection: By simulating the temporal and spatial variation trends of phosphorus load, the present invention can be used to evaluate the contributions of different pollution sources (agriculture, industry, domestic sewage) to the phosphorus output of the watershed, and further provide a scientific basis to optimize phosphorus pollution control strategies, support precise policy implementation, and contribute to the protection and restoration of the water ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flow chart of the method steps provided for this application;

[0066] Figure 2 It is a flow chart for model construction provided for this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0068] Refer to Figure 1 and Figure 2 , the embodiments of the present invention provide a method for constructing a two-level phosphorus output coefficient model at the watershed scale, including the following steps:

[0069] Step 1: Obtain relevant data of the target watershed, including digital elevation model DEM, river network water system data, phosphorus load data, soil data, meteorological data, hydrological data, and total river phosphorus concentration data.

[0070] In Step 1, the main function of Step 1 is to obtain relevant data of the target watershed, providing basic support for the calculation of the phosphorus output coefficient. First, based on the digital elevation model DEM data, the topographic information of the watershed is extracted, and the topographic slope and aspect are calculated to analyze the influence of topography on phosphorus migration. The river network water system data is parsed using GIS technology to extract the river length from the sub-watershed outlet to the total watershed outlet and river network density to characterize the carrying capacity of the river channel for phosphorus transport. At the same time, collect phosphorus load data , including agricultural non-point source pollution, point source pollution, and soil phosphorus reserves. Among them, the point source phosphorus load mainly comes from sewage treatment plants and industrial wastewater emissions, and the phosphorus content and emission data can be obtained from relevant environmental monitoring stations; the non-point source phosphorus load includes phosphorus emission coefficients, areas, yields, and population data from sources such as agricultural planting, livestock and poultry breeding, aquaculture, and domestic sewage to characterize the contributions of different sources to the phosphorus load.

[0071] In addition, to further improve the accuracy of the model, soil data including total soil phosphorus content, tillage layer depth, density, and soil mobile phosphorus load were obtained in the embodiments , to evaluate the storage and migration ability of phosphorus in the soil. Meanwhile, meteorological data such as rainfall , temperature, and evaporation were collected to simulate the impact of rainfall runoff on phosphorus transport. The collection of hydrological data included the flow at the sub-basin outlet and the total flow at the basin outlet , and combined with the total phosphorus concentration data of the river water, including the total phosphorus concentration at the sub-basin outlet and the total phosphorus concentration at the basin outlet , to ensure the accuracy of phosphorus output calculation.

[0072] Step 2: Calculate the phosphorus output coefficient of the sub-basin and the phosphorus output coefficient of the river channel , and respectively evaluate the transmission and attenuation of phosphorus load at the sub-basin outlet and the river channel.

[0073] In Step 2, first, calculate the phosphorus output coefficient of the sub-basin . It is used to characterize the ratio of the phosphorus load at the sub-basin outlet to the source phosphorus load and the soil mobile phosphorus load, and its calculation formula is as follows:

[0074] ;

[0075] where represents the phosphorus output coefficient of the th year and the th sub-basin, with a range of [0, 1], taking 0 if less than 0 and 1 if greater than 1. The phosphorus output load at the sub-basin outlet is obtained by multiplying the flow at the sub-basin outlet by the total phosphorus concentration :

[0076] ;

[0077] where is measured by the hydrological monitoring station and represents the flow at the sub-basin outlet, while is obtained by water sample analysis and represents the total phosphorus concentration at the sub-basin outlet. In addition, the source phosphorus load of the sub-basin includes the phosphorus content in agricultural non-point source pollution, industrial emissions, and domestic sewage, while the soil mobile phosphorus load is calculated from soil phosphorus reserve data, tillage layer depth, and soil density. These data are obtained through long-term monitoring and database analysis and provide input parameters for the model.

[0078] Secondly, calculate the phosphorus output coefficient of the river channel . It is used to quantify the loss of phosphorus caused by sedimentation, adsorption and degradation during the river channel transportation process. Its calculation is based on the phosphorus reduction coefficient of the river channel, that is, the average reduction rate of phosphorus load during the transmission process per kilometer of the river channel. The calculation formula is as follows:

[0079] ;

[0080] ;

[0081] Among them, is the phosphorus reduction coefficient of the river channel in the th year, indicating the degree of phosphorus attenuation per unit distance; is the phosphorus output coefficient of the river channel in the th year and the th sub-watershed, and its range is limited to [0,1]. The total phosphorus output load at the total outlet of the watershed is calculated through the total outlet flow of the watershed and the total phosphorus concentration :

[0082] ;

[0083] Among them, is obtained from the flow monitoring data at the outlet of the watershed, while is measured by water quality detection equipment. represents the river channel length from the outlet of the th sub-watershed to the total outlet of the watershed. This data is obtained from GIS river network analysis and is used to calculate the transmission loss of phosphorus in the water system.

[0084] Finally, the phosphorus output load data is obtained by simulating with a distributed process model. This model takes the sub-watershed as the calculation unit and simulates the migration and output process of phosphorus in the watershed. First, combined with hydrological data, land use information and meteorological data, it simulates the scouring and transportation of phosphorus by rainfall runoff; secondly, using sedimentation, diffusion and adsorption models, it simulates the retention and reduction of phosphorus in the river channel. The simulation results of the model are calibrated by measured data to ensure the accuracy and applicability of the calculation.

[0085] 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 channel distance.

[0086] In this embodiment, first construct a sub-watershed phosphorus output coefficient regression model, which is based on the rainfall , terrain slope and river network density Calculations are carried out. Specifically, multiple sub - basins within the study area are selected, rainfall data over the years are collected, and Geographic Information System GIS is used to extract slope and river network density parameters. Through regression analysis, a calculation formula for the phosphorus export coefficient of the sub - basin is established , and a significance test is conducted to screen out the variables that have a significant impact on phosphorus export, and finally the optimal regression equation is determined. In addition, in order to verify the accuracy of the model, the cross - validation method is used to calculate the mean square error MSE and the coefficient of determination to ensure the reliability of the model.

[0087] Subsequently, a regression model for the phosphorus export coefficient of the river channel is constructed. This model combines rainfall , topographic slope and river channel length for calculation to determine the phosphorus export behavior in the river channel. The specific steps include: collecting rainfall data of different river reaches within the study basin, using GIS technology to calculate the average slope from the outlet of the sub - basin to the total outlet of the basin, and determining the river channel length and phosphorus export along the way through a hydrological model. Based on these data, a non - linear regression method is used to fit the calculation formula , and parameter optimization and significance tests are carried out. Finally, the uncertainty of the model is evaluated through Monte Carlo simulation and verified using historical data, and the prediction errors of existing empirical models are compared to improve the applicability and stability of the regression model.

[0088] Based on the above two regression models, this method is used to predict the phosphorus export characteristics under different hydro - meteorological conditions and improve the accuracy of phosphorus load estimation. During the application process, first, data collection and pre - processing are carried out for the target basin, then the phosphorus export coefficient model of the sub - basin is used to calculate the phosphorus export volume of each sub - basin, then the phosphorus transport and attenuation are simulated by combining with the phosphorus export coefficient model of the river channel, and finally the estimated value of the phosphorus load at the total outlet of the basin is obtained. The prediction accuracy of the model is evaluated by comparing with the observed data, and simulation analysis is carried out for different rainfall scenarios and land use changes, which can provide a scientific basis for watershed phosphorus pollution control and water resources management.

[0089] Step 4: Use the two - level phosphorus export coefficient model to calculate the phosphorus export load at the total outlet of the basin and analyze the spatio - temporal variation trend of the phosphorus export load.

[0090] In Step 4, first, the total outlet phosphorus export load of the basin for a specific year is calculated based on the constructed regression model. Specifically, historical hydrological, meteorological, and land use data of the study basin are selected to determine the number of sub - basins within the basin , and the source phosphorus load and soil mobile phosphorus load of each sub - basin are collected. The phosphorus export coefficient of the sub - basin is calculated using the aforementioned regression model and the phosphorus output coefficient of the river channel , and substitute it into the calculation formula to calculate the total export phosphorus output load for that specific year. During the calculation process, multi-source data fusion technology is adopted to improve the integrity of the data, and the uncertainty of the calculation results is evaluated through error analysis methods. At the same time, to ensure the rationality of the calculation, monitoring data under different rainfall scenarios are selected for comparative verification to adjust the regression model parameters and improve the calculation accuracy.

[0091] Secondly, analyze the dynamic change trends of phosphorus output loads in different years to identify the temporal characteristics of phosphorus pollution. By organizing the hydrological data of multiple years, calculate the for each year respectively, and use time series analysis methods (such as moving average method or trend analysis) to evaluate the long-term change patterns of phosphorus output loads. Further, by comparing the meteorological data with the land use change data, explore the influence degrees of rainfall intensity, land development, and agricultural fertilization on phosphorus output loads, and analyze whether the change trends in different years are related to specific hydrological events (such as extreme rainfall or drought). In addition, use statistical analysis methods to calculate the inter-annual coefficient of variation to evaluate the volatility of phosphorus output loads, and combine with watershed management measures to study whether there is a control effect of human intervention on phosphorus loads.

[0092] Finally, evaluate the contribution of specific sources to the total phosphorus output load and study its spatial distribution characteristics. First, divide the study watershed into multiple sub-watersheds, and compare the and relative contribution rates to identify the main phosphorus source areas. Combining GIS spatial analysis technology, draw the spatial distribution map of phosphorus output loads, and use methods such as Kriging interpolation to analyze the spatial gradient changes of phosphorus loads within the watershed. In addition, further analyze the contribution rates of phosphorus loads from different sources such as agricultural land, domestic sewage, and industrial emissions, and study the influence degree of land use change on phosphorus output through sensitivity analysis.

[0093] Step Five: Adjust the model parameters based on the measured data to verify and optimize the constructed model.

[0094] In Step Five, first use the measured data to verify the constructed two-level phosphorus output coefficient model and calculate the error between the model calculated value and the measured value. Specifically, select the hydrological monitoring data of different years within the study watershed, including the measured total export phosphorus output load of the watershed , and compare and analyze it with the phosphorus output load calculated by the model. To quantitatively evaluate the error of the model, calculate the root mean square error :

[0095] ;

[0096] Among them, is the number of observed data, is the measured phosphorus load value at the th observation time point, is the model calculation value. Through the root mean square error calculation result, the prediction error of the model is evaluated, and a scatter comparison chart is drawn to visually analyze the deviation between the measured value and the calculated value. In addition, to further verify the applicability of the model, error analysis is carried out for different hydrological years (wet year, normal year, dry year) respectively to examine the adaptability and stability of the model under different hydrological conditions.

[0097] Secondly, the model parameters are adjusted based on regression analysis to improve the applicability and accuracy of the model. First, sensitivity analysis is carried out on the model input parameters (such as rainfall , terrain slope , river network density , etc.) to identify the key variables that have a greater impact on the model output, and the model parameters are adjusted based on statistical regression methods (such as least squares method, multiple regression analysis, etc.). Specifically, parameter optimization is carried out for the sub-basin phosphorus output coefficient regression model and the channel phosphorus output coefficient regression model to minimize the mean square error between the calculation result and the measured value. 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 basin conditions. The optimized model will perform phosphorus load calculation again and compare with the measured data to ensure that the adjusted model can more accurately reflect the actual situation of phosphorus output.

[0098] Finally, the cross-validation method is used to evaluate the stability of the model and optimize the weights of the key influencing factors to improve the reliability of the 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; Secondly, in each iteration process, the weights of the key influencing factors of the model (such as the regression coefficients of variables such as rainfall, terrain slope, river network density, etc.) are adjusted to identify the optimal parameter combination; Finally, statistical indicators such as the root mean square error , mean square error MSE, etc. are calculated, and an error distribution map is drawn to evaluate the stability and generalization ability of the model on different data sets. In addition, the Monte Carlo simulation method is used to analyze the uncertainty of the model, evaluate the impact of the fluctuation of input parameters on the predicted result of the output phosphorus load, and optimize the weight allocation strategy of the model to ensure that it can maintain a high prediction accuracy and stability under various environmental conditions.

[0099] It should be noted that, without conflict, the embodiments in the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0100] Obviously, the embodiments described above are only a part of the embodiments of the present invention, rather than all the 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 disclosed content of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure made by using the content of the specification and drawings of the present invention, directly or indirectly applied in other related technical fields, is equally within the scope of protection of the present invention.

Claims

1. A method for constructing a two - level output coefficient model of phosphorus at the basin scale, characterized in that, Including the following steps: S1. Obtain relevant data of the target basin, including digital elevation model (DEM), river network data, phosphorus load data, soil data, meteorological data, hydrological data, and total phosphorus concentration data of river water; S2. Calculate the phosphorus export coefficient of the sub-watershed and the phosphorus export coefficient of the river channel , and respectively evaluate the transmission and attenuation of phosphorus load at the outlet of the sub-watershed and in the river channel; S3. Construct a two-level phosphorus output coefficient regression model, and establish a general phosphorus output estimation model based on rainfall, terrain slope, river network density, and river channel distance; S4. Use the two-level phosphorus output coefficient regression model to calculate the total phosphorus output load at the basin outlet, and analyze the temporal and spatial variation trends of the phosphorus output load; S5. Validate and optimize the constructed model by adjusting the model parameters based on measured data; Calculating the phosphorus export coefficient of the sub - watershed in step S2 and the phosphorus export coefficient of the river channel , which specifically includes: (1)Calculation of the phosphorus export coefficient of the sub-watershed is based on the ratio of the phosphorus export load at the outlet of the sub-watershed to its source phosphorus load and the soil mobile phosphorus load. The calculation formula is as follows: ； Among them, is the phosphorus output coefficient of the th sub-watershed in the year, with a range of [0, 1]. If it is less than 0, take 0; if it is greater than 1, take 1. is the phosphorus output load at the outlet of the year's sub-watershed, and the calculation formula is: ； is the outlet flow of the -th year for the -th sub-basin; is the total phosphorus concentration at the outlet of the -th year for the -th sub-basin; is the source phosphorus load of the th year and the th sub-watershed; For the year's soil mobile phosphorus load in the (2)Calculation of the phosphorus output coefficient of the river channel is based on the phosphorus reduction coefficient of the river channel, which represents the reduction rate per kilometer of the phosphorus load during the river channel transmission process. The calculation formula is as follows: ； ； Among them, is the phosphorus reduction coefficient of the river channel in the th year, is the phosphorus output load at the outlet of the th sub-basin in the th year; is the phosphorus output coefficient of the river channel in the th year and the th sub-watershed, with a range of [0, 1]. If it is less than 0, take 0; if it is greater than 1, take 1. For the total phosphorus output load at the outlet of the basin in the year, the calculation formula is as follows: ； Among them, is the total export flow of the basin in the th year, is the total phosphorus concentration at the total export of the basin in the th year; is the channel length from the outlet of the sub-watershed to the total outlet of the watershed; (3) The phosphorus output load data is obtained by simulating with a distributed process model. Taking sub-basins as units, simulate the migration and output processes of phosphorus in sub-basins and river channels; The specific steps for calculating the total phosphorus output load at the basin outlet according to the two-level phosphorus output coefficient regression model in step S4 include: (1) Based on the constructed regression model, select the historical hydrological, meteorological, and land use data of the study basin to determine the number of sub-basins within the basin , and collect the source phosphorus load of each sub-basin and the soil mobile phosphorus load . Use the regression model obtained in step S2 to calculate the phosphorus export coefficient of the th year of the th sub-basin and the river channel phosphorus export coefficient of the th year of the th sub-basin Calculate the total export phosphorus output load of the basin in a specific year. The calculation formula is as follows: ； Among them, is the number of sub-basins; (2) Analyze the dynamic variation trends of the phosphorus output load in different years; (3) Evaluate the contribution of specific sources to the total phosphorus output load, and study its spatial distribution characteristics.

2. The method for constructing a two-level output coefficient model of phosphorus in a watershed scale according to claim 1, characterized in that The specific steps for obtaining relevant data of the target basin in step S1 include: Obtain the topographic information of the watershed based on the digital elevation model (DEM) and calculate the topographic slope and aspect; Analyze river network water system data using GIS technology to extract the river length from the outlet of the sub-basin to the total outlet of the basin and river network density ; Collect phosphorus load data , the phosphorus load data includes agricultural non-point source pollution, point source pollution and soil phosphorus reserves. Among them, the point source phosphorus load includes the phosphorus content and discharge volume in the wastewater discharged from sewage treatment plants and industries, and the non-point source phosphorus load includes the phosphorus discharge coefficients, areas, yields and population data in agricultural planting, livestock and poultry breeding, aquaculture and domestic sewage; Soil data, including total phosphorus content of soil, tillage layer depth, density and soil mobile phosphorus load ; Obtain meteorological data, including rainfall , temperature, and evaporation amount for simulating the phosphorus migration process; Collect hydrological data, where the hydrological data includes the flow at the outlet of the sub-basin and the total flow at the outlet of the basin , and combine the total phosphorus concentration data of the river water to provide basic data for phosphorus output calculation. The total phosphorus concentration data of the river water includes the total phosphorus concentration at the outlet of the sub-basin and the total phosphorus concentration at the outlet of the basin .

3. A method for constructing a two-level output coefficient model of phosphorus in a watershed scale according to claim 1, characterized in that, The specific steps for constructing the two-level phosphorus output coefficient regression model in step S3 include: (1)Sub - basin phosphorus output coefficient regression model, combined with rainfall , terrain slope and river network density , establish a calculation formula through regression analysis: ； Among them, is the rainfall of the th year in the th sub-watershed, is the terrain slope of the th sub-watershed, is the river network density of the th sub-watershed; (2)Regression model of phosphorus output coefficient of river channel, combined with rainfall , topographic slope and the river channel length of the sub-basin , to determine the phosphorus output behavior of the river channel. The calculation formula is as follows: ； Among them, is the river channel rainfall in the year, is the topographic slope of the th sub-basin to the main river channel at the basin outlet, is the th sub-basin to the distance of the main river channel at the basin outlet; (3) The regression model is used to predict the phosphorus output characteristics under different basin conditions, and improve the accuracy of phosphorus load estimation.

4. A method for constructing a two-level output coefficient model of phosphorus in a watershed scale, as claimed in claim 1, wherein The specific steps of step S5 include: (1) Validate the constructed model with measured data, and compare the error between the model calculated value and the measured value. The calculation formula is as follows: ； Among them, is the number of observed data, is the measured data, is the model calculated value; (2) Adjust the model parameters based on regression analysis to improve the applicability and accuracy of the model; (3) Use the cross-validation method to evaluate the stability of the model, and optimize the weights of key influencing factors to enhance the reliability of model prediction.

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