CN115330239A - Method for identifying agricultural non-point source pollution risk assessment key source area based on SWMM model - Google Patents

Method for identifying agricultural non-point source pollution risk assessment key source area based on SWMM model Download PDF

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CN115330239A
CN115330239A CN202211020694.6A CN202211020694A CN115330239A CN 115330239 A CN115330239 A CN 115330239A CN 202211020694 A CN202211020694 A CN 202211020694A CN 115330239 A CN115330239 A CN 115330239A
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赵起超
曹淑钧
金永涛
方小云
杨秀峰
段龙方
檀海兵
赵龙海
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Aolai Guoxin Beijing Testing & Detection Technology Co ltd
North China Institute of Aerospace Engineering
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Abstract

The invention discloses an identification method of a key source area for agricultural non-point source pollution risk assessment based on an SWMM model, which comprises the following steps: preprocessing the high-resolution remote sensing image and classifying land utilization; hydrologic analysis is carried out by adopting digital elevation data, the range of a drainage basin is determined, and sub drainage basins are divided; constructing an SWMM model, which mainly comprises basin generalization, model rainfall station attribute establishment, sub-catchment area attribute establishment, canal attribute establishment, land utilization attribute establishment and pollutant attribute establishment; calculating a total nitrogen factor, a total phosphorus factor, a total chemical oxygen demand factor and a rainfall runoff factor of a unit area; and calculating the risk index, grading by adopting a natural discontinuity point grading method, and identifying a key source area. The method can effectively identify the key source area, and is simpler and faster.

Description

Method for identifying key source area of agricultural non-point source pollution risk assessment based on SWMM model
Technical Field
The invention relates to the technical field of agricultural non-point source pollution risk assessment, in particular to an identification method of an agricultural non-point source pollution risk assessment key source area based on an SWMM model.
Background
The agricultural non-point source pollution amount is large, the area is wide, the space difference is large, the centralized disposal and management can not be obtained like a point source, and the treatment difficulty and cost are higher. With the rapid development of geographic information systems and technologies, multidisciplinary cross research becomes a hotspot for researching agricultural non-point source pollution, the technology and the agricultural non-point source pollution research are combined, the spatial distribution and the potential risk distribution of the pollutant load of the agricultural non-point source pollution are quantitatively researched, a key area for controlling the agricultural non-point source pollution is determined according to the risk factors of the agricultural non-point source pollution, and hierarchical and regional management is carried out, so that the efficiency of agricultural non-point source pollution management can be improved, and the agricultural environment protection work is promoted.
The current agricultural non-point source pollution risk evaluation methods comprise an output coefficient method, a non-point source pollution quantitative model method and an index system method. The output coefficient method has simple structure, needs less collected data, can directly evaluate the load quantity of the non-point source pollutant, but needs a large amount of actual measurement data on the regional scale. The non-point source pollution quantitative model mainly comprises a SWAT model, a GWLF model, an HSPF model and the like, more parameters are needed, the regional difference is large, the data accumulation in the agricultural aspect is not rich enough, and the difficulty in obtaining ground basic information is increased. The index system method can comprehensively analyze main factors influencing loss of agricultural non-point source pollutants, has better adaptability, can provide a more reasonable evaluation frame for agricultural non-point source pollution risks, and has stronger flexibility. But the multi-factor evaluation method does not consider the problems of incomplete factor selection, less pollution source classification and the like.
Although the scholars at home and abroad carry out extensive and intensive research on the problem of non-point source pollution and obtain remarkable results, the research on the process mechanism of formation, migration, conversion and load generation is not completely clear, and the requirements of agricultural non-point source pollution in different types of areas cannot be met because no centralized and unified non-point source pollution evaluation index and quantitative evaluation method exist. At present, a SWAT model is adopted to simulate the non-point source pollution load so as to identify a key source area, but the SWAT model has more parameter requirements, higher data requirement precision and higher acquisition difficulty. The SWMM model was introduced by the United states environmental protection agency in the 70 th 20 th century, is often used for predicting the fields of pipe network siltation, pipe network pollution source detection and flood disaster analysis, and Pathirana and other cases in Barceli are used as researches for evaluating flood disaster cost in urban drainage network optimization planning by developing a two-dimensional drainage model and realizing 1-D/2-D coupling with SWMM. Majunhua etc. uses a certain district confluence system drainage pipe network to carry out SWMM simulation and finds out its main overflow bottleneck node, optimizes the back secondary simulation to the pipe network and proves optimization transformation validity. No scholars have yet studied the application of the SWMM model in the assessment of risk of non-point source contamination.
Disclosure of Invention
The invention aims to solve the technical problem of how to provide a method for identifying the critical source area for agricultural non-point source pollution risk assessment, which can effectively identify the critical source area, is simpler and has higher speed.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: an agricultural non-point source pollution risk assessment key source area identification method based on an SWMM model is characterized by comprising the following steps:
preprocessing the high-resolution remote sensing image and classifying land utilization;
performing hydrological analysis by adopting digital elevation data, determining a watershed range, and dividing sub watersheds;
constructing an SWMM model, which mainly comprises basin generalization, model rainfall station attribute establishment, sub-catchment area attribute establishment, canal attribute establishment, land utilization attribute establishment and pollutant attribute establishment;
calculating a total nitrogen factor, a total phosphorus factor, a total chemical oxygen demand factor and a rainfall runoff factor of a unit area;
and calculating the risk index, grading by adopting a natural discontinuity point grading method, and identifying a key source area.
The further technical scheme is that in the step of constructing the SWMM model:
the ArcGIS and the SWMM do not have a direct data interface, an input data of the SWMM model needs an inp file, and an ArcGIS generation file is an shp file, so that the SWMM cannot directly acquire ArcGIS layer data, the ArcGIS layer data and the ArcGIS layer data need a data format conversion, and the shp is converted into the inp file to convert the layer analysis data in the shp file into the inp file format, which is embodied in the SWMM, so that model construction is performed;
the same rainfall time sequence is set in the basin range: generalizing a river network into a pipe canal in an SWMM model, arranging nodes at a river channel inflow point, an outflow point, a river channel intersection and a position where a section is mutated, and arranging the node which is converged into a jing river as a drainage port of a river basin in the SWMM model;
on the basis of sub-basins divided by using a GIS, sub-catchment areas are further divided by combining river network distribution, ground topography and land utilization types in the basins, and the runoff of each sub-catchment area designates an outflow node or flows out to other sub-catchment areas;
according to the land utilization type of the drainage basin, the water area part is removed from the sub-catchment area and divided into five categories of forest land, cultivated land, grassland, bare land and residential area, and corresponding pollutant accumulation function and scouring function are respectively set for each land utilization type.
The specific method for calculating the total nitrogen factor, the total phosphorus factor, the total chemical oxygen demand factor and the rainfall runoff factor in unit area comprises the following steps:
calculating a total nitrogen factor value of a unit area according to the ratio of the total nitrogen load to the area of the sub-basin, and performing standardized dimensional processing, wherein the formula is as shown in the following formula (1):
Figure BDA0003813916040000031
in the formula (1), Q i Is the normalized value of the i-th order of the evaluation factor; x is the number of i Is the i-th level code value of the evaluation factor; x is a radical of a fluorine atom max Is the evaluation factor maximum code value; x is the number of min Is the evaluation factor second code value.
2) And calculating the total phosphorus factor value of the unit area according to the ratio of the total phosphorus load to the sub-basin area, and performing standardized dimension processing.
3) And calculating the total COD factor value of the unit area according to the ratio of the total COD load to the sub-basin area, and performing standardized dimensional treatment.
4) And taking the rainfall runoff of the maximum recurrence period as a rainfall runoff factor value, and carrying out standardized dimensional treatment.
The further technical scheme is that the risk index is calculated in the step, a natural discontinuity grading method is adopted for grading, and the specific method for identifying the key source area comprises the following steps:
giving weight to the selected non-point source pollution risk assessment model factor according to an expert scoring method;
calculating a risk evaluation index PNPI, carrying out risk classification by adopting a natural breakpoint classification method Jenks, dividing into five grades, determining a key source area, and calculating the risk evaluation index according to the following formula (2):
Figure BDA0003813916040000032
in the formula (2), PNPI represents a non-point source pollution risk index; p i Represents the ith evaluation index; w is a group of i The weight of the i-th rating indicator is represented.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: according to the method, the SWMM model with less parameter requirements is used and a risk index method is combined for grading, so that the key source area is effectively identified, the method is simpler, the non-point source pollution condition of the research unit can be rapidly analyzed, risk evaluation is carried out, and the key source area is identified.
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The invention is described in further detail below with reference to the drawings and the detailed description.
FIG. 1 is a flow chart of a method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Generally, as shown in fig. 1, an embodiment of the invention discloses a method for identifying a critical source region for agricultural non-point source pollution risk assessment based on an SWMM model, which comprises the following steps:
the method comprises the following steps of S1, preprocessing a high-resolution remote sensing image and performing classification operation on land utilization;
s2, hydrologic analysis is carried out by adopting digital elevation data, a drainage basin range is determined, and sub-drainage basins are divided;
s3, constructing an SWMM model, which mainly comprises drainage basin generalization, and establishing a model rainfall station attribute, a sub-catchment area attribute, a pipe canal attribute, a land utilization attribute and a pollutant attribute;
s4, calculating a total nitrogen factor, a total phosphorus factor, a total chemical oxygen demand factor and a rainfall runoff factor of a unit area;
and S5, calculating the risk index, grading by adopting a natural discontinuous point grading method (Jenks), and identifying a key source area.
Further, the step S1 specifically includes the following steps:
s1-1) carrying out radiometric calibration on the high-resolution image to obtain a surface reflectivity image;
s1-2) performing atmospheric correction on the earth surface reflectivity image subjected to radiometric calibration;
s1-3) performing orthorectification operation on the remote sensing image by adopting an image self-contained rpb file;
s1-4) fusing the high-resolution multispectral image and the panchromatic image by adopting a data fusion method;
s1-5) cutting the fused image by adopting a basin range vector file;
s1-6) carrying out land utilization classification on the cut images, and dividing the images into six types of water areas, cultivated land, bare land, residential areas, forest land and grassland.
Further, the step S2 specifically includes the following steps:
s2-1) carrying out depression processing on the digital elevation data, reducing the calculation influence of other depressions and false depressions generated by data precision on the water flow direction, and ensuring the stability of grid flow direction calculation;
s2-2) performing flow direction calculation on the digital elevation data subjected to the depression processing, and outputting a flow direction grid data diagram of an area by using a flow direction analysis tool;
and S2-3) calculating the confluence rate of each grid according to the water flow direction. Calculating the number of upstream grids converged into each grid on the basis of determining the flow direction of each grid, namely the convergence rate;
s2-4) reclassifying and screening the confluence grids, and extracting river networks by taking 15000 grids with confluence areas as thresholds;
s2-5) determining the positions of flow points according to the river channel identified by the flow grid graph, and dividing a basin range by adopting a watershed algorithm;
and S2-6) taking the river network junction as a node, adopting a watershed algorithm, and taking river network data and confluence volume grids as data input to divide sub-watersheds.
Further, the step S3 specifically includes the following steps:
s3-1) a direct data interface does not exist between ArcGIS and SWMM, an inp file is needed for inputting data by an SWMM model, and an ArcGIS generated file is an shp file, so that the SWMM cannot directly acquire ArcGIS layer data, the ArcGIS layer data and the ArcGIS layer data need a data format conversion, shp is converted into inp, then layer analysis data in the shp file can be converted into the inp file format which is embodied in the SWMM, and then model construction is carried out;
and S3-2) setting the same rainfall time sequence in the basin range. Generalizing the river network into a canal in the SWMM model, arranging nodes at the inflow point, the outflow point, the river channel intersection and the position where the section is suddenly changed, and arranging the node at the river channel position as a drainage port of a river basin in the SWMM model;
s3-3) on the basis of sub-basins divided by using the GIS, further dividing the sub-catchment areas by combining river network distribution, ground topography and land utilization types in the basins, wherein the runoff of each sub-catchment area designates an outflow node or flows out to other sub-catchment areas;
and S3-4) removing the water area part in the sub-catchment area according to the land utilization type of the drainage basin, dividing the water area part into five categories of forest land, cultivated land, grassland, bare land and residential area, and setting a corresponding pollutant accumulation function and a corresponding scouring function for each land utilization type respectively.
Further, the step S4 specifically includes the following steps:
s4-1) calculating a total nitrogen factor value of unit area according to the ratio of the total nitrogen load to the area of the sub-basin, and carrying out standardized dimensional processing, wherein the formula is as shown in the following formula (1):
Figure BDA0003813916040000061
in the formula (1), Q i Is the normalized value of the i-th order of the evaluation factor; x i Is the i-th level coding value of the evaluation factor; x max Is the evaluation factor maximum code value; x min Is the evaluation factor second code value.
S4-2) calculating a total phosphorus factor value of a unit area according to the ratio of the total phosphorus load to the sub-basin area, and carrying out standardized dimensional processing.
S4-3) calculating the total COD factor value of unit area according to the ratio of the total COD load to the sub-basin area, and carrying out standardized dimensional treatment.
And S4-4) taking the rainfall runoff of the maximum recurrence period as a rainfall runoff factor value, and performing standardized dimensional treatment.
Further, the step S5 specifically includes
S5-1) weighting the selected non-point source pollution risk assessment model factors according to an expert scoring method;
s5-2) calculating a risk evaluation index (PNPI), carrying out risk classification by adopting a natural discontinuity point classification method (Jenks), dividing into five grades, determining a key source area, and calculating the risk evaluation index according to the following formula (2):
Figure BDA0003813916040000062
in the formula (2), PNPI represents a non-point source pollution risk index; pi represents the ith evaluation index; wi represents the weight of the ith rating index.
In conclusion, the method provided by the invention estimates the load of the non-point source pollution pollutants by using the SWMM model, combines a multi-factor comprehensive evaluation method and a non-point source pollution risk index method (PNPI) to carry out non-point source pollution risk assessment and identify the key source area, is simpler, and can rapidly analyze the non-point source pollution condition of the research unit, carry out risk assessment and identify the key source area.

Claims (6)

1. A method for identifying a key source area of agricultural non-point source pollution risk assessment based on an SWMM model is characterized by comprising the following steps:
preprocessing the high-resolution remote sensing image and classifying the land utilization;
performing hydrological analysis by adopting digital elevation data, determining a watershed range, and dividing sub watersheds;
constructing an SWMM model, which mainly comprises basin generalization, model rainfall station attribute establishment, sub-catchment area attribute establishment, canal attribute establishment, land utilization attribute establishment and pollutant attribute establishment;
calculating a total nitrogen factor, a total phosphorus factor, a total chemical oxygen demand factor and a rainfall runoff factor of a unit area;
and calculating the risk index, grading by adopting a natural discontinuity point grading method, and identifying a key source area.
2. The SWMM model-based method for identifying the key source regions for agricultural non-point source pollution risk assessment according to claim 1, wherein the specific method for preprocessing the high-resolution remote sensing image and performing classification operation on land utilization in the steps is as follows:
radiometric calibration is carried out on the high-resolution image to obtain a surface reflectivity image;
atmospheric correction is carried out on the earth surface reflectivity image subjected to radiometric calibration;
carrying out orthorectification operation on the remote sensing image by adopting an image-carried rpb file;
fusing the multispectral image and the panchromatic image with high resolution by adopting a data fusion method;
cutting the fused image by using the watershed range vector file;
and carrying out land utilization classification on the cut images, wherein the images are divided into six types of water areas, cultivated lands, bare lands, residential areas, forest lands and grasslands.
3. The SWMM model-based method for identifying the critical source area for agricultural non-point source pollution risk assessment as claimed in claim 1, wherein the step of performing hydrological analysis by using digital elevation data to determine the scope of the drainage basin, and the specific method for dividing the sub-drainage basins is as follows:
performing hole filling processing on the digital elevation data;
calculating the flow direction of the digital elevation data subjected to the hole filling processing, and outputting a flow direction grid data diagram of an area by using a flow direction analysis tool;
calculating the confluence amount of each grid according to the water flow direction, and calculating the number of upstream grids confluent into each grid on the basis of determining the flow direction of each grid, namely the confluence amount;
reclassifying and screening the confluence grids, and extracting river networks by taking 15000 grids with confluence areas as thresholds;
determining the positions of flow points according to the river channels identified by the flow grid maps, and dividing basin ranges by adopting a watershed algorithm;
and taking the river network junction as a node, adopting a watershed algorithm, and taking the river network data and the confluence grid as data input to divide the sub-basin.
4. The SWMM model-based method for identifying the critical source region for agricultural non-point source pollution risk assessment as claimed in claim 1, wherein in the step of constructing the SWMM model:
there is no direct data interface between ArcGIS and SWMM, the SWMM model input data needs. Inp file, and the ArcGIS generating file is. Shp file, so SWMM can not directly obtain ArcGIS layer data, both data need a data format conversion, from. Shp to. Inp, the layer analysis data in the shp file can be converted into. Inp file format embodied in SWMM, further model construction is carried out;
the same rainfall time sequence is set in the basin range: generalizing the river network into a canal in the SWMM model, arranging nodes at the inflow point, the outflow point, the river channel intersection and the position where the section is suddenly changed, and arranging the node at the river channel position as a drainage port of a river basin in the SWMM model;
on the basis of sub-basins divided by using a GIS, sub-catchment areas are further divided by combining river network distribution, ground topography and land utilization types in the basins, and the runoff of each sub-catchment area designates an outflow node or flows out to other sub-catchment areas;
according to the land utilization type of the drainage basin, the water area part is removed from the sub-catchment area and divided into five categories of forest land, cultivated land, grassland, bare land and residential area, and corresponding pollutant accumulation function and scouring function are respectively set for each land utilization type.
5. The method for identifying the key source area for agricultural non-point source pollution risk assessment based on the SWMM model according to claim 1, wherein the specific method for calculating the total nitrogen factor, the total phosphorus factor, the total chemical oxygen demand factor and the rainfall runoff factor in unit area in the step comprises the following steps:
calculating the total nitrogen factor value of unit area according to the ratio of the total nitrogen load to the sub-basin area, and carrying out standardized dimensional processing, wherein the formula is as the following formula (1):
Figure FDA0003813916030000021
in the formula (1), Q i Is the normalized value of the i-th order of the evaluation factor; x is the number of i Is the i-th level coding value of the evaluation factor; x is the number of max Is the evaluation factor maximum code value; x is the number of min Is the evaluation factor second code value.
2) And calculating the total phosphorus factor value of the unit area according to the ratio of the total phosphorus load to the sub-basin area, and performing standardized dimension processing.
3) And calculating the total COD factor value of the unit area according to the ratio of the total COD load to the sub-basin area, and performing standardized dimensional treatment.
4) And taking the rainfall runoff in the maximum recurrence period as a rainfall runoff factor value to carry out standardized dimensional treatment.
6. The method for identifying the key source area for the agricultural non-point source pollution risk assessment based on the SWMM model as claimed in claim 1, wherein the step of calculating the risk index and classifying the risk index by using a natural break point classification method comprises the following specific steps:
giving weight to the selected non-point source pollution risk assessment model factor according to an expert scoring method;
calculating a risk evaluation index PNPI, carrying out risk classification by adopting a natural breakpoint classification method Jenks, dividing into five grades, determining a key source area, and calculating the risk evaluation index according to the following formula (2):
Figure FDA0003813916030000031
in the formula (2), PNPI represents a non-point source pollution risk index; p i Represents the ith evaluation index; w is a group of i Representing the weight of the ith rating indicator.
CN202211020694.6A 2022-08-24 2022-08-24 Method for identifying agricultural non-point source pollution risk assessment key source area based on SWMM model Pending CN115330239A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116384630A (en) * 2023-03-31 2023-07-04 西北农林科技大学 Method for estimating agricultural non-point source pollution load in intersection area based on mechanism model

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116384630A (en) * 2023-03-31 2023-07-04 西北农林科技大学 Method for estimating agricultural non-point source pollution load in intersection area based on mechanism model
CN116384630B (en) * 2023-03-31 2024-01-23 西北农林科技大学 Method for estimating agricultural non-point source pollution load in intersection area based on mechanism model

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