Estimation method of non-point source phosphorus pollution load in watershed based on landscape resistance-output coefficient

Abstract

The present invention discloses a method for estimating the non-point source phosphorus pollution load of a watershed based on a landscape resistance-output coefficient, comprising: constructing a basic database of a research watershed; obtaining the non-point source phosphorus pollution load before improvement based on the basic database; constructing a landscape resistance coefficient evaluation index system, and calculating the landscape resistance coefficient affecting non-point source phosphorus pollution using a spatial principal component analysis method; calculating the landscape resistance affecting non-point source phosphorus pollution using the landscape resistance coefficient and a minimum cumulative resistance model; and calculating the improved non-point source phosphorus pollution load using the landscape resistance and the non-point source phosphorus pollution load before improvement. The present invention improves the estimation accuracy of the non-point source phosphorus pollution load of a large-scale watershed by evaluating the landscape resistance of surface natural environmental factors and estimating the non-point source phosphorus pollution load.

Description

Technical Field

[0001] The invention relates to the technical field of non-point source pollution estimation, and in particular to a method for estimating basin non-point source phosphorus pollution load based on landscape resistance-output coefficient. Background Art

[0002] With the increasing development and utilization of environmental resources by humans, excessive phosphorus is input into water bodies through non-point sources or point source discharges, leading to serious eutrophication of water bodies. Compared with point source phosphorus pollution, the control and management of non-point source phosphorus pollution still faces huge challenges, mainly because of its wide spatial distribution and uncertainty. Therefore, accurate accounting of non-point source phosphorus pollution load is an important prerequisite for achieving efficient control of non-point source pollution based on large-scale watersheds. The randomness and discontinuity of non-point source phosphorus pollution, the complexity and uncertainty of migration and conversion determine the difficulty of estimating and preventing its pollution load. Therefore, model simulation is an important means to monitor and quantify phosphorus pollution load.

[0003] The output coefficient model is a classic representative model in the empirical model. It is widely used and ignores the complex hydrological and pollutant migration and conversion process. It has a simple structure, less data requirements, and acceptable simulation accuracy. It is particularly suitable for larger basins with lack of data. In the process of implementing the technical method of the embodiment of the present invention, the inventor of this patent found that at least the following technical problems exist in the prior art:

[0004] The output coefficient model is a lumped model that cannot characterize the spatial distribution of nonpoint source pollution in large-scale watersheds. In addition, the existing improved models only improve the output coefficient model by characterizing the spatial heterogeneity of watershed characteristics, while ignoring the comprehensive impact of natural environmental factors.

[0005] In summary, the existing output coefficient model cannot meet the actual needs of quantifying phosphorus pollution loads. Summary of the invention

[0006] The embodiment of the present invention provides a method for estimating the watershed non-point source phosphorus pollution load based on landscape resistance-output coefficient, which solves the problem that the existing output coefficient model cannot meet the actual phosphorus pollution load quantification requirements.

[0007] The embodiment of the present invention provides a method for estimating watershed non-point source phosphorus pollution load based on landscape resistance-output coefficient, comprising:

[0008] Build a basic database for the watershed research;

[0009] Based on the basic database, obtain the non-point source phosphorus pollution load before improvement;

[0010] A landscape resistance coefficient evaluation index system was constructed, and the landscape resistance coefficient affecting non-point source phosphorus pollution was calculated using spatial principal component analysis.

[0011] The landscape resistance coefficient and the minimum cumulative resistance model were used to calculate the landscape resistance that affects non-point source phosphorus pollution.

[0012] The non-point source phosphorus pollution load after improvement was calculated using landscape resistance and the non-point source phosphorus pollution load before improvement.

[0013] Optionally, the non-point source phosphorus pollution load before the improvement is calculated by the following formula:

[0014] ,

[0015] In the formula, L is the non-point source phosphorus pollution load before improvement, K These are the types of pollution sources from livestock and poultry breeding, residents’ lives, and land use; E k It is k Livestock and poultry breeding, residents' life, k Phosphorus export coefficient for each land use, A k It is k The number of livestock and poultry breeding, the number of residents living, k Area of ​​land use.

[0016] Optionally, the landscape resistance coefficient evaluation index system includes: relative elevation, slope, surface roughness, water flow length, vegetation coverage, annual rainfall, terrain wetness index, soil erodibility, and soil leaching index.

[0017] Optionally, the use of spatial principal component analysis to calculate the landscape resistance coefficient affecting non-point source phosphorus pollution specifically includes:

[0018] The spatial principal component analysis method is used to remove the correlation or redundancy of each indicator in the landscape resistance coefficient evaluation index system through orthogonal rotation transformation, and a new spatial principal component is obtained.

[0019] The spatial principal components with cumulative contribution rates greater than 90% were selected and weighted summed to obtain the landscape resistance coefficient affecting non-point source phosphorus pollution.

[0020] Optionally, the landscape resistance coefficient affecting non-point source phosphorus pollution is calculated by the following formula:

[0021] ,

[0022] ,

[0023] In the formula, RE is the landscape resistance coefficient, P x For the xA spatial principal component with a cumulative contribution rate greater than 90%; W x For the x The weight of the spatial principal component with a cumulative contribution rate greater than 90%; F x For the x The common factor variance of the spatial principal components with cumulative contribution rates greater than 90%, m is the number of spatial principal components whose cumulative contribution rate is greater than 90%.

[0024] Optionally, the landscape resistance affecting non-point source phosphorus pollution is calculated by the following formula:

[0025] ,

[0026] In the formula, MCR To investigate the landscape resistance to non-point source phosphorus pollution, f The negative correlation between landscape resistance and ecological suitability affecting non-point source phosphorus pollution is min Take the minimum cumulative resistance for different sources for a certain landscape unit, D ij From the source landscape unit i To the target landscape unit j The spatial distance RE i Source landscape unit i Landscape resistance coefficient to phosphorus export process, n is the number of target landscape units, l is the number of source landscape units.

[0027] Optionally, the improved non-point source phosphorus pollution load is calculated by the following formula:

[0028] ,

[0029] In the formula, is the improved non-point source pollutant load, Norm(MCR) j For the j The normalized landscape resistance value of a landscape unit.

[0030] Optionally, the construction of a basic database for the research basin specifically includes:

[0031] Get the study area K Phosphorus export coefficients of various pollution sources, land use data, digital elevation, hydrological stations, river network data, district and county administrative division vector data, vegetation coverage, rainfall data from meteorological stations, average monthly runoff data, rural and urban permanent population data from the census, and livestock and poultry breeding data from the statistical yearbook.

[0032] Optionally, after constructing the basic database of the research basin, the method further includes:

[0033] Resample raster spatial data such as land use data, digital elevation, and vegetation cover to a uniform grid size, and convert all spatial data to a unified projection coordinate system;

[0034] Delineate sub-basins based on hydrological stations, digital elevation and river network data;

[0035] Based on the rainfall data from meteorological stations, the Kriging co-spatial interpolation method was used to obtain the annual rainfall data of the study basin;

[0036] The rural and urban permanent population data from the census and the livestock and poultry breeding data from the statistical yearbook are entered into the attribute table of the district and county administrative division vector data.

[0037] Optionally, after calculating the improved nonpoint source phosphorus pollution load, also include:

[0038] The total phosphorus flux measured at the outlet of the study basin was estimated using the following calculation formula:

[0039] ,

[0040] In the formula, L 0 is the measured total phosphorus flux from non-point sources, C i is the measured total phosphorus concentration, Q i is the measured monthly average runoff;

[0041] Based on the measured total phosphorus flux from non-point sources and the improved non-point source phosphorus pollution load, the relative error is calculated using the following formula:

[0042] ,

[0043] In the formula, Re is the relative error of the model simulation.

[0044] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0045] Since the surface landscape is composed of many natural environmental factors, there is spatial heterogeneity, and the non-point source phosphorus output process is a complex complex of multiple factors and multiple processes. If we look at the internal mechanism of the pattern-process coupling system, as long as there is flow, there must be resistance to prevent its flow, so there will also be resistance in the migration and conversion process of phosphorus in the surface landscape. Therefore, phosphorus needs to pass through different landscape space units to reach the water body from the pollution source along with material carriers such as water flow and soil particles, and will be subject to the spatial resistance of certain landscape units on the migration path. Therefore, the non-point source phosphorus output process can be considered as a dissipative flow process in which phosphorus nutrients overcome landscape resistance. The spatial difference in landscape resistance will inevitably cause spatial differences in the difficulty of phosphorus pollutants entering the water body, which will affect the phosphorus output of the surface landscape and ultimately affect the occurrence and development of the phosphorus output process. Therefore, the present invention improves the estimation accuracy of the non-point source phosphorus pollution load in large-scale watersheds by evaluating the landscape resistance of surface natural environmental factors and estimating the non-point source phosphorus pollution load. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a method for estimating watershed non-point source phosphorus pollution load based on landscape resistance-output coefficient in one embodiment of the present invention;

[0047] Figure 2 An elevation map of a certain study basin in one embodiment of the present invention;

[0048] Figure 3 A slope map of a certain study basin in one embodiment of the present invention;

[0049] Figure 4 A surface roughness map of a certain study basin in one embodiment of the present invention;

[0050] Figure 5 A water flow length diagram of a certain study watershed in one embodiment of the present invention;

[0051] Figure 6 A land use distribution map of a certain research basin in one embodiment of the present invention;

[0052] Figure 7 A sub-basin diagram of a certain study basin in one embodiment of the present invention;

[0053] Figure 8 It is an annual rainfall distribution map of a certain research basin in one embodiment of the present invention;

[0054] Fig. 9 It is a distribution map of non-point source phosphorus pollution load of livestock and poultry breeding, residents' life and land use in a sub-basin of a certain study basin before improvement in one embodiment of the present invention;

[0055] Fig.10A vegetation coverage distribution map of a certain research basin in one embodiment of the present invention;

[0056] Fig.11 A distribution map of terrain moisture index of a certain research basin in one embodiment of the present invention;

[0057] Fig.12 A soil erodibility distribution map of a certain study basin in one embodiment of the present invention;

[0058] Fig.13 A soil leaching index distribution map of a certain research basin in one embodiment of the present invention;

[0059] Fig.14 A distribution diagram of landscape resistance coefficient of a certain research basin in one embodiment of the present invention;

[0060] Fig.15 A source distribution map of a certain research basin in one embodiment of the present invention;

[0061] Fig.16 A landscape resistance distribution map of paddy fields, dry land and residential land in a certain study basin in one embodiment of the present invention;

[0062] Fig.17 A landscape resistance distribution map of a certain research basin in one embodiment of the present invention;

[0063] Fig.18 This is a distribution diagram of non-point source phosphorus pollution load for livestock and poultry breeding, residents' life, and land use in a sub-basin of a certain research basin after improvement in one embodiment of the present invention. DETAILED DESCRIPTION

[0064] The embodiment of the present invention provides a method for estimating the watershed non-point source phosphorus pollution load based on landscape resistance-output coefficient, which solves the problem that the existing output coefficient model cannot meet the actual phosphorus pollution load quantification requirements.

[0065] In order to better understand the above-mentioned method for estimating the watershed non-point source phosphorus pollution load based on the landscape resistance-output coefficient, a detailed description will be given below in conjunction with the drawings and specific implementation methods of the specification. Obviously, the embodiments described in the present invention are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] Please refer to the following Figure 1 , a method for estimating watershed non-point source phosphorus pollution load based on landscape resistance-output coefficient in an embodiment of the present invention includes steps S1 to S5.

[0067] Step S1: Construct the basic database of the research basin, including: obtaining K Phosphorus export coefficients of various pollution sources, land use data, digital elevation, hydrological stations, river network data, district and county administrative division vector data, vegetation coverage, rainfall data from meteorological stations, average monthly runoff data, rural and urban permanent population data from the census, and livestock and poultry breeding data from the statistical yearbook.

[0068] After building the basic database of the study basin in step S1, it also includes: resampling raster spatial data such as land use data, digital elevation, and vegetation coverage into a unified raster size, and converting all spatial data into a unified projection coordinate system; dividing sub-basins based on hydrological stations, digital elevation, and river network data; using the Kriging collaborative spatial interpolation method to obtain annual rainfall data for the study basin based on rainfall data from meteorological stations; entering rural and urban permanent population data from the census and livestock and poultry breeding data from the statistical yearbook into the attribute table of the district and county administrative division vector data.

[0069] In the specific implementation process of step S1, for example, obtaining a certain research basin such as Figure 2 Based on the digital elevation, obtain Figure 3 The slope shown, Figure 4 The surface roughness shown, Figure 5 The length of the water flow shown. Figure 6 Land use data, digital elevation, and Fig.10 All raster spatial data such as vegetation coverage shown in the figure were resampled to a uniform raster size, and all spatial data were converted to a uniform projection coordinate system. Based on hydrological stations, digital elevation data and river network data, the study area was divided into the following basins: Figure 7 The five sub-basins shown are sub-basin 1, sub-basin 2, sub-basin 3, sub-basin 4 and sub-basin 5, totaling 532 sub-basins, each with an area greater than 100 km 2 . Use Kriging co-spatial interpolation to obtain a study area such as Figure 8 The annual rainfall data for 2020 is shown in the figure. Obtain the 2020 district and county rural and urban permanent population data and livestock and poultry breeding data involved in the basin of a certain study area, and enter the rural and urban permanent population data of the census and the livestock and poultry breeding data of the statistical yearbook into the attribute table of the district and county administrative division vector data.

[0070] Step S2: Based on the basic database, obtain the non-point source phosphorus pollution load before improvement. The non-point source phosphorus pollution load before improvement is calculated by the following formula:

[0071] ,

[0072] In the formula,L is the non-point source phosphorus pollution load before improvement, in kg / a; K These are the types of pollution sources from livestock and poultry breeding, residents’ lives, and land use; E k It is k Livestock and poultry breeding, residents' life, k The phosphorus output coefficient of each land use represents the output of different nutrients in the study basin, and the units are kg / (head·a), kg / (person·a), kg / (hm 2 a); A k It is k The number of livestock and poultry breeding, the number of residents living, k The area of ​​land use is hm 2 , head, person.

[0073] In the specific implementation process of step S2, for example, based on land use data, rural and urban permanent population data from the census, and livestock and poultry breeding data from the statistical yearbook, the land use area, the number of livestock and poultry breeding, and the number of residents living in a certain research basin are determined.

[0074] Determine the phosphorus output coefficients of major pollution sources, specifically the phosphorus output coefficients of livestock and poultry breeding, residents’ lives, and land use. See the table below for details.

[0075]

[0076] Then, based on the Johnes output coefficient model and the phosphorus output coefficients of livestock and poultry breeding, residents’ lives, and land use in a certain study basin, the non-point source phosphorus pollution load of the study basin before the improvement of livestock and poultry breeding, residents’ lives, and land use was calculated. Next, it is theoretically assumed that there is a certain relationship between the occupied area and the amount of pollutants generated, and the non-point source phosphorus output of the district and county is allocated to each sub-basin according to the area ratio relationship, and the following is obtained: Fig. 9 The non-point source phosphorus output from livestock and poultry breeding, residents’ life, and land use in each sub-basin is shown.

[0077] Step S3: Construct a landscape resistance coefficient evaluation index system, and use the spatial principal component analysis method to calculate the landscape resistance coefficient that affects non-point source phosphorus pollution. The landscape resistance coefficient reflects the comprehensive impact of landscape units on the non-point source phosphorus pollution in the watershed through natural environmental factors. As a dynamic process, non-point source phosphorus output is controlled by many natural environmental factors, mainly including topography, landform, vegetation coverage, rainfall, soil and other aspects. These natural environmental factors control the phosphorus output in the process of non-point source pollution. They play different resistance roles in the process of non-point source phosphorus output, that is, there are differences in landscape resistance coefficients. Among them, the landscape resistance coefficient evaluation index system includes: relative elevation, slope, surface roughness, water flow length, vegetation coverage, annual rainfall, terrain wetness index, soil erodibility, and soil leaching index.

[0078] In the specific implementation process, for example, based on the actual situation of a certain study basin, the data sources of natural environmental factors affecting nonpoint source phosphorus output are decomposed into 9 indicators, namely relative elevation, slope, surface roughness, water flow length, vegetation coverage, annual rainfall, terrain wetness index, soil erodibility, and soil leaching index. Figure 2 As shown in Figure 2, the slope of a watershed in a study area is Figure 3 As shown in Figure 2, the surface roughness of a watershed in a study area is as follows: Figure 4 As shown in Figure 2, the water flow length of a watershed in a study area is as follows: Figure 5 As shown in Figure 2, the vegetation coverage of a watershed in a study area is as follows: Fig.10 As shown in Figure 2, the annual rainfall in a watershed in a study area is as follows: Figure 8 As shown in Figure 2, the topographic wetness index of a watershed in a study area is as follows: Fig.11 As shown in Figure 2, the soil erodibility of a watershed in a study area is as follows: Fig.12 As shown in Figure 2, the soil leaching index of a watershed in a study area is as follows: Fig.13 As shown in the figure, the nine indicators together constitute the landscape resistance coefficient evaluation index system.

[0079] The calculation formula of surface roughness is as follows:

[0080] ,

[0081] In the formula, M is the surface roughness; β is the slope in degrees.

[0082] The calculation formula of terrain wetness index is as follows:

[0083] ,

[0084] In the formula, T is the terrain wetness index at a certain point; α is the cumulative water catchment at a certain point, in mm;P is the annual rainfall in mm; β is the slope value in degrees.

[0085] The calculation formula of soil leaching index is as follows:

[0086] ,

[0087] ,

[0088] ,

[0089] ,

[0090] Where LI is the soil leaching index; PI is the rainfall spatial distribution index; SI is the seasonal distribution index; prec, R , prec(ls) are annual rainfall, annual soil interception capacity and total rainfall in the non-flood season from October of the current year to April of the following year, and their units are mm / a, mm / a, mm respectively; CN value is a comprehensive parameter reflecting the pre-rainfall period in the study basin and is dimensionless.

[0091] The spatial principal component analysis method is used to calculate the landscape resistance coefficient that affects non-point source phosphorus pollution. Specifically, the method includes: using the spatial principal component analysis method, through orthogonal rotation transformation, removing the correlation or redundancy of each indicator in the landscape resistance coefficient evaluation index system, and obtaining a new spatial principal component; selecting the spatial principal components with a cumulative contribution rate greater than 90%, and performing weighted sum to obtain the landscape resistance coefficient that affects non-point source phosphorus pollution, such as Fig.14 shown.

[0092] The landscape resistance coefficient that affects non-point source phosphorus pollution is calculated as follows:

[0093] ,

[0094] ,

[0095] In the formula, RE is the landscape resistance coefficient, P x For the x A spatial principal component with a cumulative contribution rate greater than 90%; W x For the x The weight of the spatial principal component with a cumulative contribution rate greater than 90%; F x For the x The common factor variance of the spatial principal components with cumulative contribution rates greater than 90%, m is the number of spatial principal components whose cumulative contribution rate is greater than 90%.

[0096] Step S4: Using the landscape resistance coefficient and the minimum cumulative resistance model, calculate the landscape resistance that affects nonpoint source phosphorus pollution, which reflects the relative or absolute difficulty of various "flows" (matter and energy, etc.) from the source to overcome the landscape resistance to reach the destination, and also objectively reflects the trend and potential possibility of the spatial movement of things. The landscape resistance calculation formula is as follows:

[0097] ,

[0098] In the formula, MCR To influence the landscape resistance of non-point source phosphorus pollution; f The negative correlation between landscape resistance and ecological suitability that affects nonpoint source phosphorus pollution is a functional relationship without a specific value. min Take the minimum value of cumulative resistance for different sources for a certain landscape unit; D ij From the source landscape unit i To the target landscape unit j spatial distance; RE i Source landscape unit i Landscape resistance coefficient to phosphorus export process; n is the number of target landscape units; l is the number of source landscape units.

[0099] In the specific implementation process, for example: paddy fields, dry land and residential land in a certain study area are selected as the sources of non-point source phosphorus pollution, such as Fig.15 Then, the landscape resistance is obtained according to the minimum cumulative resistance model. Specifically, the cost distance tool of ArcGIS platform is used to obtain the landscape resistance of paddy fields, dry land and residential land respectively, as shown in Fig.16 Then the Con function in the raster calculator of ArcGIS platform was used to identify the minimum landscape resistance as the landscape resistance affecting nonpoint source phosphorus loss, as shown in Fig.17 shown.

[0100] Step S5: Calculate the improved non-point source phosphorus pollution load using the landscape resistance and the non-point source phosphorus pollution load before improvement. The calculation formula is as follows:

[0101] ,

[0102] In the formula, is the improved non-point source phosphorus pollution load, Norm(MCR) j For the j The normalized landscape resistance value of a landscape unit.

[0103] In the specific implementation process, for example: the raster calculator of the ArcGIS platform is used to normalize the landscape resistance raster data by the maximum and minimum values, and the average landscape resistance of each sub-basin is calculated, and then multiplied by the non-point source phosphorus pollution load of each sub-basin before improvement to obtain the improved non-point source phosphorus pollution load of each sub-basin, such as Fig.18 shown.

[0104] In order to verify the feasibility of the present invention, the actual measured non-point source total phosphorus flux at the outlet of the study basin is used to test the calculation accuracy of the improved non-point source phosphorus pollution load. After calculating the improved non-point source phosphorus pollution load in step S5, it also includes: estimating the actual measured non-point source total phosphorus flux at the outlet of the study basin, and the calculation formula is as follows:

[0105] ,

[0106] In the formula, L 0 is the measured total phosphorus flux from non-point sources, C i is the measured total phosphorus concentration, Q i is the measured monthly average runoff;

[0107] Based on the measured total phosphorus flux from non-point sources and the improved non-point source phosphorus pollution load, the relative error is calculated using the following formula:

[0108] ,

[0109] In the formula, Re is the relative error of the model simulation.

[0110] In the specific implementation process, for example: the total phosphorus flux of the five sub-basins entering the study basin is estimated by using the measured monthly average runoff and total phosphorus concentration of the river section at the outlet of the five sub-basins in the study area. The non-point source phosphorus pollution load before and after the improvement is calculated, and the relative error is calculated respectively. The results show that the absolute values ​​of the relative errors of the non-point source phosphorus pollution load before the improvement and the measured non-point source total phosphorus flux in Basin 5, Basin 4, Basin 3, Basin 2 and Basin 1 in a certain study area are 9.74%, 19.70%, 20.78%, 88.04% and 126.26% respectively, and the relative errors of the non-point source phosphorus pollution load after the improvement and the measured non-point source total phosphorus flux in Basin 5, Basin 4, Basin 3, Basin 2 and Basin 1 in a certain study area are 6.01%, 1.54%, 2.95%, 1.95% and 8.94% respectively, which can illustrate that the present invention can improve the estimation accuracy of non-point source phosphorus pollution load in large-scale basins.

[0111] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for estimating watershed non-point source phosphorus pollution load based on landscape resistance-output coefficient, characterized in that: include: Build a basic database for the study basin; Based on the basic database, obtain the non-point source phosphorus pollution load before improvement; A landscape resistance coefficient evaluation index system is constructed, and the landscape resistance coefficient affecting non-point source phosphorus pollution is calculated using the spatial principal component analysis method; the landscape resistance coefficient evaluation index system includes: relative elevation, slope, surface roughness, water flow length, vegetation coverage, annual rainfall, terrain wetness index, soil erodibility, and soil leaching index; The landscape resistance coefficient and the minimum cumulative resistance model are used to calculate the landscape resistance that affects non-point source phosphorus pollution. The calculation formula for the landscape resistance that affects non-point source phosphorus pollution is as follows: ， In the formula, MCR To investigate the landscape resistance to non-point source phosphorus pollution, f The negative correlation between landscape resistance and ecological suitability affecting non-point source phosphorus pollution is min Take the minimum cumulative resistance for different sources for a certain landscape unit, D ij From the source landscape unit i To the target landscape unit j The spatial distance RE i Source landscape unit i Landscape resistance coefficient to phosphorus export process, n is the number of target landscape units, l is the number of source landscape units; The improved non-point source phosphorus pollution load is calculated by using the landscape resistance and the non-point source phosphorus pollution load before the improvement; the improved non-point source phosphorus pollution load is calculated by the following formula: ， In the formula, is the improved non-point source pollutant load, Norm(MCR) j For the j The normalized landscape resistance value of each landscape unit is K It is the pollution source type of livestock and poultry breeding, residents' life and land use. E k It is k Livestock and poultry breeding, residents' life, k Phosphorus export coefficient for each land use, A k It is k The number of livestock and poultry breeding, the number of residents living, k Area of ​​land use.

2. The method according to claim 1, characterized in that The calculation formula of the non-point source phosphorus pollution load before the improvement is as follows: ， In the formula, L is the non-point source phosphorus pollution load before improvement, K These are the types of pollution sources from livestock and poultry breeding, residents’ lives, and land use; E k It is k Livestock and poultry breeding, residents' life, k Phosphorus export coefficient for each land use, A k It is k The number of livestock and poultry breeding, the number of residents living, k Area of ​​land use.

3. The method according to claim 2, characterized in that The method of calculating the landscape resistance coefficient affecting non-point source phosphorus pollution by using the spatial principal component analysis method specifically includes: The spatial principal component analysis method is used to remove the correlation or redundancy of each indicator in the landscape resistance coefficient evaluation index system through orthogonal rotation transformation, and a new spatial principal component is obtained. The spatial principal components with cumulative contribution rates greater than 90% were selected and weighted summed to obtain the landscape resistance coefficient affecting non-point source phosphorus pollution.

4. The method according to claim 3, characterized in that The calculation formula of the landscape resistance coefficient affecting non-point source phosphorus pollution is as follows: ， ， In the formula, RE is the landscape resistance coefficient, P x For the x A spatial principal component with a cumulative contribution rate greater than 90%; W x For the x The weight of the spatial principal component with a cumulative contribution rate greater than 90%; F x For the x The common factor variance of the spatial principal components with cumulative contribution rates greater than 90%, m is the number of spatial principal components whose cumulative contribution rate is greater than 90%.

5. The method according to claim 1, characterized in that The construction of the basic database for the research basin specifically includes: Get the study area K Phosphorus export coefficients of various pollution sources, land use data, digital elevation, hydrological stations, river network data, district and county administrative division vector data, vegetation coverage, rainfall data from meteorological stations, average monthly runoff data, rural and urban permanent population data from the census, and livestock and poultry breeding data from the statistical yearbook.

6. The method according to claim 5, characterized in that After constructing the basic database of the research basin, it also includes: Resample raster spatial data such as land use data, digital elevation, and vegetation cover to a uniform grid size, and convert all spatial data to a unified projection coordinate system; Delineate sub-basins based on hydrological stations, digital elevation and river network data; Based on the rainfall data from meteorological stations, the Kriging co-spatial interpolation method was used to obtain the annual rainfall data of the study basin; The rural and urban permanent population data from the census and the livestock and poultry breeding data from the statistical yearbook are entered into the attribute table of the district and county administrative division vector data.

7. The method according to claim 4, characterized in that After calculating the improved non-point source phosphorus pollution load, it also includes: The total phosphorus flux measured at the outlet of the study basin was estimated using the following calculation formula: ， In the formula, L 0 is the measured total phosphorus flux from non-point sources, C i is the measured total phosphorus concentration, Q i is the measured monthly average runoff; Based on the measured total phosphorus flux from non-point sources and the improved non-point source phosphorus pollution load, the relative error is calculated using the following formula: ， In the formula, Re is the relative error of the model simulation.

Images