CN111882182A - Agricultural non-point source pollution risk diagnosis method suitable for irrigation area - Google Patents
Agricultural non-point source pollution risk diagnosis method suitable for irrigation area Download PDFInfo
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Abstract
The invention discloses an agricultural non-point source pollution risk diagnosis method suitable for an irrigation area, which comprises the following steps: collecting and processing information; researching agricultural non-point source pollution migration rules and identifying influence factors in the irrigation area; determining influence factors which have relatively large influence on agricultural non-point source pollution in an irrigation area, and respectively calculating the influence factors to form a factor space database; carrying out standardization and dimensionality reduction on the influence factors in the factor database, and converting the influence factors into 2-3 principal components; determining an agricultural non-point source pollution risk diagnosis formula in the irrigation area, calculating an agricultural non-point source pollution risk index of each sub-drainage basin, and performing grade division; simulating by using a distributed SWAT model to obtain an agricultural non-point source pollution risk index simulation result of each sub-basin; and judging the accuracy of the calculation result according to the simulation result. The method can accurately diagnose the agricultural non-point source pollution risk, improve the diagnosis efficiency and feasibility and save the cost.
Description
Technical Field
The invention relates to the technical field of environmental protection, in particular to an agricultural non-point source pollution risk diagnosis method suitable for an irrigation area.
Background
With the gradual control of point source pollution in China, the problem of water environment pollution caused by agricultural non-point source pollution becomes one of the important sources of water environment pollution in China and even all countries around the world. Agricultural non-point source pollution refers to pollution caused by soil disturbance, such as soil particles, nitrogen and phosphorus, pesticides and other organic or inorganic pollutants in a farmland, entering a water body by means of farmland surface runoff, underground infiltration and the like in the process of precipitation or irrigation or random pollution discharge of livestock and poultry breeding. Due to the wide pollution range, random occurrence and uncertain discharge modes and ways, the non-point source pollution generated by agricultural activities is difficult and serious in monitoring and prevention, and scientific basis and theoretical support can be provided for the agricultural non-point source pollution prevention and treatment of the drainage basin by scientifically diagnosing the agricultural non-point source pollution risk in the drainage basin range.
At present, methods for agricultural non-point source pollution assessment are researched more, but due to different assessment mechanisms and different application ranges of different methods, the methods are obviously different, and particularly, the agricultural non-point source pollution risk diagnosis method for irrigation areas is rare. The distributed hydrological model SWAT model developed by the American Ministry of agriculture has a strong physical basis, is suitable for simulating the transportation and transformation of pollutants in a drainage basin with different underlying surface characteristics in a complex environment, and has been widely accepted in the global scope as the research of non-point source pollution. CN108647401A discloses a watershed nitrogen and phosphorus pollution assessment method based on a space remote sensing technology, the method adopts a method of combining typical small watershed data acquisition and full watershed SWAT model simulation, non-point source nitrogen and phosphorus pollution is assessed based on soil moisture, but the method only assesses the pollution of a non-point source to a water environment through soil leaching, does not assess the whole process condition that the non-point source pollution enters a water body, and an assessment result is to be verified. CN102867120A discloses a non-point source pollution calculation method based on remote sensing pixels, which constructs a basin foundation database, calculates non-point source pollution loads and river entering total quantities of different pollution types, and calculates river entering coefficients of dissolved pollutants and adsorbed pollutants respectively according to the non-point source pollution loads and the river entering total quantities. A large number of agricultural non-point source pollution research methods emerge, and because China is a big agricultural country and the geographic environment is complex, the difficulty of directly transplanting foreign models is increased, and the difficulty of agricultural non-point source pollution risk diagnosis is increased.
At present, researches on the generation amount, the discharge amount and the river-entering load amount of agricultural non-point source pollutants in an irrigation area are more, but researches on agricultural non-point source pollution risk diagnosis of the irrigation area are less. According to the national conditions, the method system for accurately and comprehensively diagnosing the agricultural non-point source pollution risk of the irrigation area is provided by combining the advantages of spatial information and a mathematical statistics method.
Disclosure of Invention
Aiming at the existing problems, the invention aims to provide an agricultural non-point source pollution risk diagnosis method suitable for an irrigation area, which is used for evaluating and grading agricultural non-point source pollution risks in the irrigation area and verifying the evaluation result by comparing a model simulation result, so that the accuracy of agricultural non-point source pollution risk diagnosis of the irrigation area is improved, and a method reference is provided for agricultural non-point source pollution evaluation in areas lacking data.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for diagnosing agricultural non-point source pollution risk suitable for irrigation areas is characterized by comprising the following steps,
s1: collecting and processing information, namely collecting remote sensing images, land utilization types and meteorological data in the irrigation area, hydrological water quality data at an outlet of a drainage ditch and source data of agricultural non-point source pollution;
s2: researching agricultural non-point source pollution migration rules and identifying influence factors in the irrigation area;
s3: determining influence factors which have relatively large influence on agricultural non-point source pollution in an irrigation area, and respectively calculating the influence factors to form a factor space database;
s4: carrying out standardization and dimensionality reduction on the influence factors in the factor database, and converting the plurality of influence factors into 2-3 principal components;
s5: performing hierarchical analysis on the main components obtained in the step S4, determining an agricultural non-point source pollution risk diagnosis formula in the irrigation area, calculating an agricultural non-point source pollution risk index of each sub-drainage basin in the irrigation area, and performing level division;
s6: simulating by using a distributed SWAT model to obtain an agricultural non-point source pollution risk index simulation result of each sub-basin in the irrigation area;
s7: comparing the simulation results in the step S6, performing error analysis on the agricultural non-point source pollution risk index of each sub-basin calculated in the step S5, and judging the accuracy of the agricultural non-point source pollution risk index of each sub-basin calculated in the step S5.
Further, the specific operation of step S2 includes,
s21: analyzing the information and data in the irrigation area collected in the step S1, and dividing the irrigation area into a plurality of sub-watersheds;
s22: carrying out similarity analysis on all sub-watersheds, selecting the most representative sub-watersheds with abundant data as typical sub-watersheds, carrying out runoff plot experiments, and recording rainfall, gradient, soil property, agricultural non-point source pollutant leaching infiltration and runoff loss data along with surface runoff;
s23: analyzing the migration and transformation rules and main loss carriers of agricultural non-point source pollutants in the typical sub-flow area under different rainfalls, gradients and soil properties;
s24: and identifying influence factors causing agricultural non-point source pollution loss according to the migration transformation rule research result analyzed in the step S23.
Further, the specific operation of step S3 includes:
s31: determining an influence factor which has relatively large influence on the agricultural non-point source pollution by using the migration and transformation rule of the agricultural non-point source pollutant in the typical sub-flow domain obtained in the step S2;
s32: and (4) calculating each influence factor which has relatively large influence on the agricultural non-point source pollution in the step S31 by utilizing the research results of domestic and foreign scholars on the influence factors which have relatively large influence on the agricultural non-point source pollution in the irrigation area determined in the step S31, and the migration and transformation rules and the main loss carrier data of the agricultural non-point source pollutant under different rains, gradients and soil attributes collected in the steps S22 and S23 to form a factor space database.
Further, the influence factors which have relatively large influence on agricultural non-point source pollution comprise irrigation factors, surface runoff factors, underground infiltration storage factors, drainage ditch reduction factors, vegetation interception factors, soil erosion factors and effective distance factors from the drainage ditches.
Further, the calculation methods of the influence factors which have relatively large influence on the agricultural non-point source pollution are respectively as follows:
the irrigation factor expression is as follows:
wherein, CI: a water-pouring factor; l: pollution load output; GI: irrigation intensity; f: the infiltration amount; e: the evaporation capacity; s: the amount of variation in water storage of the field surface; j: represents the j irrigation;
average concentration of contaminants in field surface runoff; a and b: depending on the planting structure and the irrigation time, the value of a is 0.35-0.55, and the value of b is 1.2;
the surface runoff factor expression is as follows:
wherein P is annual rainfall; i isaλ S, λ represents the initial loss rate of rainfall; s is a parameter and is related to the underlying surface factor; CN is a standard runoff curve number, is a dimensionless parameter and reflects the characteristics of different land utilization types of the irrigation area;
the expression of the underground accumulation and seepage factor is as follows:
wherein: pannualThe annual rainfall is; pdryRainfall in non-flood season; CN is the number of standard runoff curves;
the drainage ditch reduction factor expression is as follows: k ═ v/xln (C)On the upper part/CLower part)
Wherein, COn the upper part、CLower partThe concentration of pollutants on the upper and lower sections of the drainage ditch is mg/l; v is the average flow velocity in the drainage ditch, m/s; x is the length of the drainage ditch, m; k is a drainage ditch reduction factor;
the expression of the vegetation retention factor is as follows:
wherein, TDAiThe width of the forest and grass buffer system at the point i on the downstream streamline; BT (BT)DAiThe slope angle of the point; n is the number of grids of the land utilization type of the forest land, the grassland or the water surface on a downstream streamline at a certain point on the watershed;
the soil erosion factor is calculated by adopting a general soil loss equation, and the expression is as follows:
A＝R×K×L×S×C×P
in the formula, A is the annual soil erosion amount; r is rainfall erosion force factor; k is a soil erodible factor; l is a slope length factor and is dimensionless; s is a gradient factor and is dimensionless; c is a vegetation cover and management factor and is dimensionless; p is a water and soil conservation measure factor and is dimensionless;
the effective distance factor from the drainage ditch is expressed as:
in the formula, DiRepresenting the effective distance factor from the drain, diDistance from unit i to the catchment path, -0.090533 is an empirical index.
Further, the specific operation of step S4 includes:
s41: normalizing each influence factor in the factor space database to enable different influence factors to have the same dimension and scale and eliminate difference, wherein the calculation method of the normalization comprises the following steps:
in the formula, XiRepresenting the influence factor after standardization, wherein X is the influence factor of each image element, min (X) is the minimum value of the influence factor, and max (X) is the maximum value of the influence factor;
s42: adopting principal component analysis, carrying out dimension reduction processing on the influence factors after the standardization processing, judging the correlation among the influence factors according to the distribution condition of a correlation coefficient matrix, selecting principal components with contribution rate exceeding 85%, determining the expression of each principal component according to the factor load condition, converting a plurality of influence factors into 3 principal components through orthogonal transformation, wherein the expressions of the 3 principal components are respectively as follows:
F1＝a1*CI+b1*Q+c1*LI+d1*K+e1*RI+f1*D+g1A
F2＝a2*CI+b2*Q+c2*LI+d2*K+e2*RI+f2*D+g2A
F3＝a3*CI+b3*Q+c3*LI+d3*K+e3*RI+f3*D+g3A
wherein a, b, c and D … represent the contribution rate of each influence factor, CI is the irrigation factor, Q is the surface runoff factor, LI is the underground infiltration accumulation factor, K is the drainage ditch reduction factor, RI is the vegetation interception factor, D is the effective distance factor from the drainage ditch, and A is the soil erosion factor.
Further, the specific operation of step S5 includes:
s51: determining the weight of the principal component by taking the variance distribution characteristics of each principal component in the principal component analysis in the step S4 as the basis for constructing a judgment matrix, thereby determining the agricultural non-point source pollution risk diagnosis formula in the irrigation area as
γ＝v*F1+u*F2+w*F3
Wherein v, u, and w represent the contribution rates of the respective influencing factors, and F1、F2、F3Is a main component;
s52: calculating the agricultural non-point source pollution risk index of each sub-basin in the irrigation area by using the risk diagnosis formula obtained in the step S51;
s53: and grading the agricultural non-point source pollution risk of each sub-watershed in the irrigation area.
Further, the specific operation of step S6 includes:
s61: performing agricultural non-point source pollution simulation on the nested drainage basin by using the basic data collected in the step S1 and adopting a distributed SWAT model;
s62: carrying out parameter calibration and verification on the model by utilizing the collected hydrological water quality data at the outlet of the nested basin in the SWAT-CUP;
s63: analyzing the output file of the model, and calculating the agricultural non-point source pollution risk index simulation result of each sub-drainage basin in the irrigation area by the formula
Wherein: p is a risk index of agricultural non-point source pollutants in the irrigated area, and is between 0 and 1, and L issubIs the load of pollutants at the outlet of the sub-basin; ssubAnd the load is discharged for the non-point source pollution of the sub-drainage basin.
Further, the specific operation of step S7 includes:
s71: calculating an error index H of the agricultural non-point source pollution risk index of each sub-basin obtained in the step S52 and the agricultural non-point source pollution risk index simulation result of each sub-basin obtained in the step S63,
s72: and when H is more than or equal to-30% and less than or equal to 30%, the result of the agricultural non-point source pollution risk index calculated in the step S52 is considered to be accurate.
The invention has the beneficial effects that:
1. according to the agricultural non-point source pollution risk diagnosis method suitable for the irrigation area, by developing sub-basin experimental research and carrying out fine simulation on the whole pollution transmission process, large-range data acquisition is avoided, evaluation efficiency and feasibility are improved, and cost is saved;
2. the agricultural non-point source pollution risk diagnosis method suitable for the irrigation area utilizes remote sensing and geographic space information data, improves data processing efficiency, performs data visualization expression, and has simple and understandable evaluation results;
3. the agricultural non-point source pollution risk diagnosis method suitable for the irrigation area adopts a natural breakpoint method to perform evaluation grade division, and similar values are grouped most appropriately, so that the difference among the grades is maximized, and the applicability of the evaluation grade division in the irrigation area is improved;
4. the agricultural non-point source pollution risk diagnosis method suitable for the irrigation area solves the problem that the agricultural non-point source pollution risk diagnosis is carried out in the large-range irrigation area without data, and can help a decision maker to make effective management measures.
Drawings
FIG. 1 is a flow chart of a method for diagnosing agricultural non-point source pollution risks in an irrigation area according to the present invention;
FIG. 2 is a graph of runoff and pollutant outflow data for a single rainfall event in accordance with an embodiment of the present invention;
FIG. 3 is a calculation result of a contamination risk index of total nitrogen in the example of the present invention;
FIG. 4 shows the calculation results of the contamination risk index of total phosphorus in the example of the present invention;
fig. 5 is a schematic diagram illustrating parameter calibration and verification results of the model by using collected hydrographic water quality data at the outlet of the nested watershed in the SWAT-CUP in the embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following further describes the technical solution of the present invention with reference to the drawings and the embodiments.
Example (b):
the agricultural non-point source pollution risk diagnosis method is used for carrying out agricultural non-point source pollution risk diagnosis on Ningxia bronze canyon irrigation areas.
Referring to fig. 1, the method for diagnosing the agricultural non-point source pollution risk comprises the following steps,
s1: collecting and processing information, namely collecting remote sensing images, land utilization types and meteorological data in the irrigation area, hydrological water quality data at an outlet of a drainage ditch and source data of agricultural non-point source pollution;
the agricultural non-point source pollution sources in the irrigation area are investigated, and the main pollutant sources are determined to be farmland fertilizers, livestock breeding and rural life. According to the water quality monitoring condition, the main indexes causing non-point source pollution of the watershed agriculture are total nitrogen and total phosphorus.
Further, step S2: researching agricultural non-point source pollution migration rules and identifying influence factors in the irrigation area;
specifically, S21: downloading remote sensing images, land utilization types, meteorological data and the like of the irrigation area, carrying out similarity analysis on basic data of all sub-watersheds of the irrigation area, and dividing the irrigation area into 19 sub-watersheds in ArcGIS according to DEM data, drainage ditch distribution and the like;
s22: carrying out similarity analysis on all sub-watersheds, selecting the most representative sub-watersheds with abundant data as typical sub-watersheds, carrying out runoff plot experiments, and recording rainfall, gradient, soil property, agricultural non-point source pollutant leaching infiltration and runoff loss data along with surface runoff;
specifically, when a runoff plot experiment is performed, runoff volume and pollutant outflow volume at different slopes are recorded, as shown in table 1. And the runoff and rainfall intensity of the whole process of single rainfall are recorded, as shown in the attached figure 2.
TABLE 1 pollutant outflow at different slopes
S23: analyzing the migration and transformation rules and main loss carriers of agricultural non-point source pollutants in the typical sub-flow area under different rainfalls, gradients and soil properties;
s24: and identifying influence factors causing agricultural non-point source pollution loss according to the migration transformation rule research result analyzed in the step S23.
Further, step S3: determining influence factors which have relatively large influence on agricultural non-point source pollution in an irrigation area, and respectively calculating the influence factors to form a factor space database;
specifically, S31: determining an influence factor which has relatively large influence on the agricultural non-point source pollution by using the migration and transformation rule of the agricultural non-point source pollutant in the typical sub-flow domain obtained in the step S2; the influence factors which have relatively large influence on agricultural non-point source pollution comprise irrigation factors, surface runoff factors, underground infiltration storage factors, drainage ditch reduction factors, vegetation interception factors, soil erosion factors and effective distance factors from the drainage ditches.
S32: and (4) calculating each influence factor which has relatively large influence on the agricultural non-point source pollution in the step S31 by utilizing the research results of domestic and foreign scholars on the influence factors which have relatively large influence on the agricultural non-point source pollution in the irrigation area determined in the step S31, and the migration and transformation rules and the main loss carrier data of the agricultural non-point source pollutant under different rains, gradients and soil attributes collected in the steps S22 and S23 to form a factor space database.
Specifically, the achievements of research on influence factors of scholars at home and abroad, which have relatively great influence on agricultural non-point source pollution in irrigation areas, include: the main relationship between the water inlet amount of the agricultural non-point source pollutant and the rainfall is exponential function, power function, logarithmic function and the like; the relation between the gradient and the water inlet amount of agricultural non-point source pollution is mainly a power function, a linear function, an exponential function and the like; the surface runoff factor conforms to an SCS-CN model; the soil erosion factor is calculated by using a USLE equation; the calculation of the underground seepage storage factor adopts a formula combining flood season and non-flood season; the determination of the influence factors can be determined according to main influence factors of non-point source pollution in the field agriculture, and is not unique, for example, irrigation areas are developed in irrigation and irrigation drainage systems are dense, pollutants mainly enter water bodies through channels, and therefore irrigation factors and channel factors can be added.
Further, the calculation methods of the influence factors which have relatively large influence on the agricultural non-point source pollution are respectively as follows:
the irrigation factor expression is as follows:
wherein, CI: a water-pouring factor; l: pollution load output; GI: irrigation intensity (cm); f: infiltration (cm); e: evaporation amount (cm); s: water storage variation (cm) of the field surface; j: represents the j-th irrigation (cm); : average concentration of contaminants in field surface runoff (field injection concentration) (mg/L); a and b: depending on the planting structure and the irrigation time, the value of a is 0.35-0.55, and the value of b is 1.2;
the surface runoff factor expression is as follows:
wherein P is annual rainfall; i isaλ S, λ represents the initial loss rate of rainfall; s is a parameter andthe underlying surface factor is related; in order to calculate S, introducing a standard runoff curve number CN, wherein CN is a dimensionless parameter and reflects the characteristics of different land utilization types of an irrigation area;
the expression of the underground accumulation and seepage factor is as follows:
wherein: pannualThe annual rainfall is; pdryRainfall in non-flood season; CN is the number of standard runoff curves;
the drainage ditch reduction factor expression is as follows: k ═ v/xln (C)On the upper part/CLower part)
Wherein, COn the upper part、CLower partThe concentration of pollutants on the upper and lower sections of the drainage ditch is mg/l; v is the average flow velocity in the drainage ditch, m/s; x is the length of the drainage ditch, m; k is a drainage ditch reduction factor;
the expression of the vegetation retention factor is as follows:
wherein, TDAiThe width of the forest and grass buffer system at the point i on the downstream streamline; BT (BT)DAiThe slope angle of the point; n is the number of grids of the land utilization type of the forest land, the grassland or the water surface on a downstream streamline at a certain point on the watershed;
the soil erosion factor is calculated by adopting a general soil loss equation, and the expression is as follows:
A＝R×K×L×S×C×P
in the formula, A is the annual soil erosion amount; r is rainfall erosion force factor; k is a soil erodible factor; l is a slope length factor and is dimensionless; s is a gradient factor and is dimensionless; c is a vegetation cover and management factor and is dimensionless; p is a water and soil conservation measure factor and is dimensionless;
the effective distance factor from the drainage ditch is expressed as:
in the formula, DiDistance indicating drainage ditchEffective distance factor of diDistance from unit i to the catchment path, -0.090533 is an empirical index.
The factor space database comprises geographic space distribution data of each influence factor, namely information of each influence factor is calculated in ArcGIS by using the expression, factor space distribution in a nested streaming domain is formed by adopting Krigin interpolation, and a rasterized space database with the pixel precision of 1km multiplied by 1km is formed. The average of the individual impact factors over the 19 substream regions is shown in table 2.
TABLE 2 statistical table of mean value of each sub-basin influence factor in irrigation district
Further, step S4: carrying out standardization and dimensionality reduction on the influence factors in the factor database, and converting the plurality of influence factors into 2-3 principal components;
specifically, S41: normalizing each influence factor in the factor space database to enable different influence factors to have the same dimension and scale and eliminate difference, wherein the calculation method of the normalization comprises the following steps:
in the formula, XiRepresenting the influence factor after standardization, wherein X is the influence factor of each image element, min (X) is the minimum value of the influence factor, and max (X) is the maximum value of the influence factor;
the results of the mean values after normalization for each influencing factor are shown in table 3.
TABLE 3 standardization of the influence factors of the sub-watersheds in the irrigation area
S42: and (3) performing dimension reduction processing and transverse elimination on the influence factors after the standardization processing by adopting principal component analysis, and judging the correlation among the influence factors according to the distribution condition of a correlation coefficient matrix, wherein the distribution condition of the correlation coefficient matrix is shown in a table 4.
Table 4 correlation coefficient matrix distribution
Generally, the correlation coefficient is more than or equal to 0.6, namely, the correlation between the two factors is considered to be stronger, the correlation between the irrigation factor and the surface runoff factor is stronger, the correlation between the surface runoff factor and the underground infiltration storage factor, the correlation between the surface runoff factor and the soil erosion factor and the correlation between the surface runoff factor and the drainage ditch reduction factor are stronger, the correlation between the underground infiltration storage factor and the soil erosion factor is stronger, the correlation between the effective distance factor from the drainage ditch and the vegetation interception factor is stronger, and the correlation between the drainage ditch reduction factor and the vegetation interception factor is stronger.
Selecting principal components with contribution rates exceeding 85% according to a total variance interpretation table, determining expressions of the principal components according to factor load conditions, converting a plurality of influence factors into 3 principal components through orthogonal transformation, recording irrigation factors, surface runoff factors, underground infiltration factors, soil erosion factors, effective distance factors from a drainage ditch, drainage ditch reduction factors and vegetation retention factors as components 1, 2, 3, 4, 5, 6 and 7 respectively, and then recording a total variance interpretation table of 7 components as shown in table 5.
TABLE 5 Total variance interpretation of different components
As can be seen from Table 5, the contribution of the first 3 components reaches 89.022%, which is higher than 85%, indicating that this principal component can reflect 89.022% of the amount of information provided by the most initial variable parameters. Starting from the 4 th component, the characteristic value is less than 1, and the main component gradually decreases in change trend. Therefore, three main components are set as a water irrigation factor, a surface runoff factor and an underground infiltration storage factor.
According to the factor load matrix in the SPSS analysis results, the coefficients corresponding to the 3 principal components are shown in table 6.
Table 6 table of coefficient of correspondence of each principal component
The expression of the three principal components obtained by multiplying the eigenvector obtained by analysis according to table 6 by matrix operation is respectively:
F1＝0.397*CI+0.105*Q+0.969*LI+0.175*K-0.013*RI+f1*D+0.979A
F2＝0.637*CI+0.917*Q-0.039*LI+0.382*K-0.024*RI+f2*D+0.088A
F3＝0.417*CI+0.128*Q+0.076*LI+0.697*K+0.965*RI+f3*D-0.034A
wherein CI is a irrigation factor, Q is a surface runoff factor, LI is an underground accumulation and seepage factor, K is a drainage ditch reduction factor, RI is a vegetation interception factor, D is an effective distance factor from a drainage ditch, and A is a soil erosion factor.
Further, step S5: performing hierarchical analysis on the main components obtained in the step S4, determining an agricultural non-point source pollution risk diagnosis formula in the irrigation area, calculating an agricultural non-point source pollution risk index of each sub-drainage basin in the irrigation area, and performing level division;
specifically, S51: and (4) taking the variance distribution characteristics of each principal component in the principal component analysis in the step S4 as a basis for constructing a judgment matrix, reducing the dependency on experts, determining the weight of the principal component, and determining an agricultural non-point source pollution risk diagnosis formula in the irrigation area.
And constructing a judgment matrix B for determining final weight according to the independent variables F1, F2 and F3, and comparing the relative membership values of the components according to the optimal process of the matrix. Under the same conditions, the membership degree of the component 1 is more varied than that of the component 2, namely, the component 1 transmits relatively more comprehensive evaluation information. Therefore, the final weight influence size is determined by comparing the standard deviation of the membership sample of 3 components, thereby constituting a judgment matrix B1 for weight assignment.
Calculated final decision matrix B1As follows
The consistency ratio of the judgment matrix is 0.0002 and λ max is 3.0002, i.e. the consistency of the judgment matrix B is acceptable, and the weight determined according to the consistency ratio is reasonable.
Therefore, the pollution risk diagnosis calculation formula of the total nitrogen is as follows:
γ＝0.4135F1+0.3826F2+0.2039F3；
wherein, F1、F2、F3Is the main component corresponding to total nitrogen;
similarly, the risk diagnosis calculation formula of total phosphorus is as follows:
γ＝0.4460A1+0.3390A2+0.2150A3；
wherein A is1、A2、A3Is the main component corresponding to total phosphorus;
s52: calculating the agricultural non-point source pollution risk index of each sub-basin in the irrigation area by using the risk diagnosis formula obtained in the step S51;
specifically, the mean values of the total nitrogen and total phosphorus contamination risk indices for 19 and the sub-basins are shown in table 7.
TABLE 7 mean value of total nitrogen and total phosphorus pollution risk index of each sub-basin in irrigation district
S53: the natural breakpoint method in ArcGIS is utilized to grade the agricultural non-point source pollution risk of each sub-basin in the irrigation area, the risk grade is divided into five grades of low, medium, high and high, and the risk classification tables of different grades are shown in table 8.
Table 8 Risk class Scale Table
And determining the risk level of each sub-basin in the irrigation area according to the risk level division table, as shown in table 9.
TABLE 9 Risk level situation of sub-watersheds in irrigation district
Further, step S6: simulating by using a distributed SWAT model to obtain an agricultural non-point source pollution risk index simulation result of each sub-basin in the irrigation area;
specifically, S61: performing agricultural non-point source pollution simulation on the nested drainage basin by using the basic data collected in the step S1 and adopting a distributed SWAT model;
s62: carrying out parameter calibration and verification on the model by utilizing the collected hydrological water quality data at the outlet of the nested basin in the SWAT-CUP; the verification results are shown in fig. 5, and it can be seen from fig. 5 that the model simulation results are well available.
S63: analyzing the output file of the model, and calculating the agricultural non-point source pollution risk index simulation result of each sub-drainage basin in the irrigation area by the formula
Wherein: p is a risk index of agricultural non-point source pollutants in the irrigated area, and is between 0 and 1, and L issubIs the load of pollutants at the outlet of the sub-basin; ssubAnd the load is discharged for the non-point source pollution of the sub-drainage basin.
The results of the simulation of the agricultural non-point source pollution risk index for each sub-watershed in the irrigation area based on the distributed SWAT model are shown in table 10.
TABLE 10 agricultural non-point source pollution risk index simulation results for each sub-basin in the irrigated area based on the distributed SWAT model
S7: comparing the simulation results in the step S6, performing error analysis on the agricultural non-point source pollution risk index of each sub-basin calculated in the step S5, and judging the accuracy of the agricultural non-point source pollution risk index of each sub-basin calculated in the step S5.
Specifically, because the simulation result of the SWAT model is relatively accurate, the simulation of the SWAT model is assumed to be the true value, relative error analysis is performed, and the agricultural non-point source pollution risk index of each sub-basin obtained in the step S52 and the error index H of the agricultural non-point source pollution risk index simulation result of each sub-basin obtained in the step S63 are calculated
And when H is more than or equal to-30% and less than or equal to 30%, the result of the agricultural non-point source pollution risk index calculated in the step S52 is considered to be accurate.
The agricultural non-point source pollution risk index obtained by the risk diagnosis method of the invention for 19 sub-watersheds is compared with the agricultural non-point source pollution risk index simulation result obtained by the SWAT model simulation, and the error index H result of each sub-watersheds is shown in Table 11.
TABLE 11 agricultural non-point source pollution risk index and SWAT model simulation result error index obtained by the method of the present invention
As can be seen from Table 11, the agricultural non-point source pollution risk index obtained by the risk diagnosis method of the present invention is compared with the SWAT model simulation result, and the errors are all within + -30%, which indicates that the accuracy of the risk index result obtained by the agricultural non-point source pollution risk diagnosis method of the present invention is high.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A method for diagnosing agricultural non-point source pollution risk suitable for irrigation areas is characterized by comprising the following steps,
s1: collecting and processing information, namely collecting remote sensing images, land utilization types and meteorological data in the irrigation area, hydrological water quality data at an outlet of a drainage ditch and source data of agricultural non-point source pollution;
s2: researching agricultural non-point source pollution migration rules and identifying influence factors in the irrigation area;
s3: determining influence factors which have relatively large influence on agricultural non-point source pollution in an irrigation area, and respectively calculating the influence factors to form a factor space database;
s4: carrying out standardization and dimensionality reduction on the influence factors in the factor database, and converting the plurality of influence factors into 2-3 principal components;
s5: performing hierarchical analysis on the main components obtained in the step S4, determining an agricultural non-point source pollution risk diagnosis formula in the irrigation area, calculating an agricultural non-point source pollution risk index of each sub-drainage basin in the irrigation area, and performing level division;
s6: simulating by using a distributed SWAT model to obtain an agricultural non-point source pollution risk index simulation result of each sub-basin in the irrigation area;
s7: comparing the simulation results in the step S6, performing error analysis on the agricultural non-point source pollution risk index of each sub-basin calculated in the step S5, and judging the accuracy of the agricultural non-point source pollution risk index of each sub-basin calculated in the step S5.
2. The agricultural non-point source pollution risk diagnosis method suitable for irrigated areas according to claim 1, wherein the specific operations of step S2 include,
s21: analyzing the information and data in the irrigation area collected in the step S1, and dividing the irrigation area into a plurality of sub-watersheds;
s22: carrying out similarity analysis on all sub-watersheds, selecting the most representative sub-watersheds with abundant data as typical sub-watersheds, carrying out runoff plot experiments, and recording rainfall, gradient, soil property, agricultural non-point source pollutant leaching infiltration and runoff loss data along with surface runoff;
s23: analyzing the migration and transformation rules and main loss carriers of agricultural non-point source pollutants in the typical sub-flow area under different rainfalls, gradients and soil properties;
s24: and identifying influence factors causing agricultural non-point source pollution loss according to the migration transformation rule research result analyzed in the step S23.
3. The agricultural non-point source pollution risk diagnosis method suitable for irrigated areas according to claim 2, wherein the specific operations of step S3 comprise:
s31: determining an influence factor which has relatively large influence on the agricultural non-point source pollution by using the migration and transformation rule of the agricultural non-point source pollutant in the typical sub-flow domain obtained in the step S2;
s32: and (4) calculating each influence factor which has relatively large influence on the agricultural non-point source pollution in the step S31 by utilizing the research results of domestic and foreign scholars on the influence factors which have relatively large influence on the agricultural non-point source pollution in the irrigation area determined in the step S31, and the migration and transformation rules and the main loss carrier data of the agricultural non-point source pollutant under different rains, gradients and soil attributes collected in the steps S22 and S23 to form a factor space database.
4. The agricultural non-point source pollution risk diagnosis method suitable for the irrigation area as claimed in claim 3, wherein the influence factors having relatively large influence on the agricultural non-point source pollution comprise irrigation factors, surface runoff factors, underground infiltration storage factors, drainage ditch reduction factors, vegetation retention factors, soil erosion factors and effective distance factors from drainage ditches.
5. The agricultural non-point source pollution risk diagnosis method suitable for the irrigation area as claimed in claim 4, wherein the calculation methods of the influence factors which have relatively large influence on the agricultural non-point source pollution are respectively as follows:
the irrigation factor expression is as follows:
wherein, CI: a water-pouring factor; l: pollution load output; GI: irrigation intensity; f: the infiltration amount; e: the evaporation capacity; s: the amount of variation in water storage of the field surface; j: represents the j irrigation;
average concentration of contaminants in field surface runoff; a and b: depending on the planting structure and the irrigation time, the value of a is 0.35-0.55, and the value of b is 1.2;
the surface runoff factor expression is as follows:
wherein P is annual rainfall; i isaλ S, λ represents the initial loss rate of rainfall; s is a parameter and is related to the underlying surface factor; CN is a standard runoff curve number, is a dimensionless parameter and reflects the characteristics of different land utilization types of the irrigation area;
the expression of the underground accumulation and seepage factor is as follows:
wherein: pannualThe annual rainfall is; pdryRainfall in non-flood season; CN is standardThe number of runoff curves;
the drainage ditch reduction factor expression is as follows: k ═ v/x ln (C)On the upper part/CLower part)
Wherein, COn the upper part、CLower partThe concentration of pollutants on the upper and lower sections of the drainage ditch is mg/l; v is the average flow velocity in the drainage ditch, m/s; x is the length of the drainage ditch, m; k is a drainage ditch reduction factor;
the expression of the vegetation retention factor is as follows:
wherein, TDAiThe width of the forest and grass buffer system at the point i on the downstream streamline; BT (BT)DAiThe slope angle of the point; n is the number of grids of the land utilization type of the forest land, the grassland or the water surface on a downstream streamline at a certain point on the watershed;
the soil erosion factor is calculated by adopting a general soil loss equation, and the expression is as follows:
A＝R×K×L×S×C×P
in the formula, A is the annual soil erosion amount; r is rainfall erosion force factor; k is a soil erodible factor; l is a slope length factor and is dimensionless; s is a gradient factor and is dimensionless; c is a vegetation cover and management factor and is dimensionless; p is a water and soil conservation measure factor and is dimensionless;
the effective distance factor from the drainage ditch is expressed as:
in the formula, DiRepresenting the effective distance factor from the drain, diDistance from unit i to the catchment path, -0.090533 is an empirical index.
6. The agricultural non-point source pollution risk diagnosis method suitable for irrigated areas according to claim 5, wherein the specific operations of step S4 comprise:
s41: normalizing each influence factor in the factor space database to enable different influence factors to have the same dimension and scale and eliminate difference, wherein the calculation method of the normalization comprises the following steps:
in the formula, XiRepresenting the influence factor after standardization, wherein X is the influence factor of each image element, min (X) is the minimum value of the influence factor, and max (X) is the maximum value of the influence factor;
s42: adopting principal component analysis, carrying out dimension reduction processing on the influence factors after the standardization processing, judging the correlation among the influence factors according to the distribution condition of a correlation coefficient matrix, selecting principal components with contribution rate exceeding 85%, determining the expression of each principal component according to the factor load condition, converting a plurality of influence factors into 3 principal components through orthogonal transformation, wherein the expressions of the 3 principal components are respectively as follows:
F1＝a1*CI+b1*Q+c1*LI+d1*K+e1*RI+f1*D+g1A
F2＝a2*CI+b2*Q+c2*LI+d2*K+e2*RI+f2*D+g2A
F3＝a3*CI+b3*Q+c3*LI+d3*K+e3*RI+f3*D+g3A
wherein a, b, c and D … represent the contribution rate of each influence factor, CI is the irrigation factor, Q is the surface runoff factor, LI is the underground infiltration accumulation factor, K is the drainage ditch reduction factor, RI is the vegetation interception factor, D is the effective distance factor from the drainage ditch, and A is the soil erosion factor.
7. The agricultural non-point source pollution risk diagnosis method suitable for irrigated areas according to claim 6, wherein the specific operations of step S5 comprise:
s51: determining the weight of the principal component by taking the variance distribution characteristics of each principal component in the principal component analysis in the step S4 as the basis for constructing a judgment matrix, thereby determining the agricultural non-point source pollution risk diagnosis formula in the irrigation area as
γ＝v*F1+u*F2+w*F3
Wherein v, u, and w represent the contribution rates of the respective influencing factors, and F1、F2、F3Is a main component;
s52: calculating the agricultural non-point source pollution risk index of each sub-basin in the irrigation area by using the risk diagnosis formula obtained in the step S51;
s53: and grading the agricultural non-point source pollution risk of each sub-watershed in the irrigation area.
8. The agricultural non-point source pollution risk diagnosis method suitable for irrigated areas according to claim 7, wherein the specific operations of step S6 comprise:
s61: performing agricultural non-point source pollution simulation on the nested drainage basin by using the basic data collected in the step S1 and adopting a distributed SWAT model;
s62: carrying out parameter calibration and verification on the model by utilizing the collected hydrological water quality data at the outlet of the nested basin in the SWAT-CUP;
s63: analyzing the output file of the model, and calculating the agricultural non-point source pollution risk index simulation result of each sub-drainage basin in the irrigation area by the formula
Wherein: p is a risk index of agricultural non-point source pollutants in the irrigated area, and is between 0 and 1, and L issubIs the load of pollutants at the outlet of the sub-basin; ssubAnd the load is discharged for the non-point source pollution of the sub-drainage basin.
9. The agricultural non-point source pollution risk diagnosis method suitable for irrigated areas according to claim 8, wherein the specific operations of step S7 comprise:
s71: calculating the agricultural non-point source pollution risk index of each sub-basin obtained in the step S52 and each sub-basin obtained in the step S63The error index H of the simulation result of the agricultural non-point source pollution risk index of the sub-drainage basin,
s72: and when H is more than or equal to-30% and less than or equal to 30%, the result of the agricultural non-point source pollution risk index calculated in the step S52 is considered to be accurate.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113011993A (en) * | 2021-03-19 | 2021-06-22 | 中国水利水电科学研究院 | Method for measuring and calculating water-entering load of agricultural pollution source based on standard data |
| CN113011992A (en) * | 2021-03-19 | 2021-06-22 | 中国水利水电科学研究院 | River basin agricultural non-point source pollution river entry coefficient measuring and calculating method based on standard data |
| CN114662930A (en) * | 2022-03-24 | 2022-06-24 | 中国科学院地理科学与资源研究所 | Agricultural non-point source pollution risk identification method and electronic equipment |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113011993A (en) * | 2021-03-19 | 2021-06-22 | 中国水利水电科学研究院 | Method for measuring and calculating water-entering load of agricultural pollution source based on standard data |
| CN113011992A (en) * | 2021-03-19 | 2021-06-22 | 中国水利水电科学研究院 | River basin agricultural non-point source pollution river entry coefficient measuring and calculating method based on standard data |
| CN113011993B (en) * | 2021-03-19 | 2024-03-12 | 中国水利水电科学研究院 | Method for measuring and calculating load of agricultural pollution source entering water based on standard data |
| CN113011992B (en) * | 2021-03-19 | 2024-03-15 | 中国水利水电科学研究院 | River basin agricultural non-point source pollution river entering coefficient measuring and calculating method based on standard data |
| CN114662930A (en) * | 2022-03-24 | 2022-06-24 | 中国科学院地理科学与资源研究所 | Agricultural non-point source pollution risk identification method and electronic equipment |
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