Method for regional agriculture non-point source risk prediction

Abstract

The invention relates to the technical field of agricultural non-point source pollution treatment, in particular to a method for predicting regional agricultural non-point source risks, which is used for predicting the pollution risks of regions possibly polluted by agricultural non-point sources by collecting existing environmental basic data and constructing a proper model. In an agricultural non-point source pollution-oriented region, the method can accurately predict the concentration of nutrient substances in water bodies in different time periods and different regions, can provide a scientific basis for optimizing agricultural management measures and reducing non-point source pollution risks, and also provides a reference for agricultural non-point source pollution assessment of other drainage basins.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural non-point source pollution control, and in particular to a method for regional agricultural non-point source risk prediction. Background Art

[0002] Agricultural non-point source pollution (ANPSP) refers to pollutants generated by agricultural activities, such as fertilization, spraying, irrigation, and livestock excrement, which enter the environment through natural processes such as precipitation, runoff, and soil infiltration, thereby causing widespread and dispersed pollution to water bodies, soil, and the atmosphere. The main sources of agricultural non-point source pollution are farmland runoff and leakage, rural surface runoff, rural domestic sewage, rural solid waste, and livestock and poultry breeding. The main environmental risks of agricultural non-point source pollution are that rich organic matter and nutrients such as nitrogen and phosphorus enter rural river waters, leading to eutrophication of water bodies, and that toxic components in pesticides and fertilizers threaten the drinking water safety of humans and animals. Assessing the risk level of agricultural non-point source pollution to surrounding water bodies is a prerequisite for all agricultural non-point source pollution prevention and control work.

[0003] The main difficulties currently faced in risk assessment of agricultural non-point source pollution include the dispersed and latent nature of pollution sources, the wide temporal and spatial scope of pollution and high randomness, the difficulty in obtaining basic data, the inaccurate simulation of pollutant migration and transformation by existing models, which is difficult to generalize to the macro scale, and the assessment process involves multiple disciplines, which increases complexity and uncertainty.

[0004] Based on this, this case is brought forward. Summary of the invention

[0005] The purpose of the present invention is to provide a simple, efficient and accurate method for predicting the risk of agricultural non-point source pollution. By collecting existing public data, establishing reasonable parameters and improving existing models, the environmental risks of agricultural non-point source pollution to surrounding water bodies in different periods and regions can be obtained.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

[0007] A method for regional agricultural non-point source risk prediction comprises the following steps:

[0008] S10.SWAT model construction;

[0009] S20. Data preparation: obtain various data of the study basin and build the spatial database and attribute database required for the operation of the SWAT model. The various data obtained include meteorological data, DEM elevation, soil data, land use data, precipitation data and runoff data;

[0010] For spatial databases, all electronic digital maps need to be processed uniformly in terms of coordinate system and projection, including the following processes:

[0011] S21. For spatial databases, outline the study area on the watershed map;

[0012] S22. Use ArcGIS to calibrate the watershed map using longitude and latitude;

[0013] S23. Create a new surface file using ArcCatalog and outline the study area;

[0014] S24. Extract the selected area according to the mask MASK;

[0015] S25. Clip the digital elevation map DEM, soil type map, and land use map in ArcGIS environment to obtain relevant digital maps of the study area;

[0016] S30.SWAT model operation: Run the model based on the initial setting parameters to simulate the basin hydrological cycle and material transport process;

[0017] S40. Calibration and verification;

[0018] S50. Result analysis.

[0019] Furthermore, for the attribute database, the soil data therein adopts the World Soil Database jointly created by FAO and IIASA.

[0020] Furthermore, for the attribute database, the meteorological data therein is generated by a weather generator that is constructed and suitable for application in the studied area.

[0021] Furthermore, in step S30, the SWAT model simulation process will automatically segment the watershed according to the digital elevation map of the study area, the terrain features and the extracted river network to achieve sub-watershed division. When performing sub-watershed division, the model threshold is selected as 2000ha.

[0022] Furthermore, when dividing the sub-basin, the basin outlet needs to be considered, and the sub-basin outlet should be set at each section based on the actual river conditions.

[0023] The advantages of the present invention are as follows: the present invention collects existing basic environmental data and constructs a suitable model to predict the pollution risk of areas that may actually be affected by agricultural non-point source pollution. The results show that in areas where agricultural non-point source pollution is dominant, the method can more accurately predict the concentration of nutrients in water bodies in different periods and regions. The present invention can provide a scientific basis for optimizing agricultural management measures and reducing the risk of non-point source pollution, and also provides a reference for the assessment of agricultural non-point source pollution in other river basins. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the flow chart of the prediction method in the embodiment;

[0025] Figure 2 The site distribution map of the water quality monitoring stations in JH City in the embodiment;

[0026] Figure 3a-3h This is the measured and simulated data of TN and TP at the JH City Water Quality Monitoring Station. Figure 3a This is the data map of the JJY region. Figure 3b This is the data map of FL area. Figure 3c This is the data map of the DGQ area. Figure 3d This is the data map for the HWQ area. Figure 3e This is the data map for the NJQ area. Figure 3f This is the data map of the TXZ area. Figure 3g This is the data map of the YDQ area. Figure 3h This is the data map for the TK region. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with the embodiments. It should be understood that the directions or positional relationships indicated by the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" herein are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention.

[0028] This embodiment proposes a method for regional agricultural non-point source risk prediction, such as Figure 1 As shown, the following steps are included.

[0029] S10. SWAT model construction.

[0030] S20. Data preparation: obtain various data of the study basin and build the spatial database and attribute database required for the operation of the SWAT model. The various data obtained include meteorological data, DEM elevation, soil data, land use data, precipitation data and runoff data.

[0031] The SWAT model relies on a number of basic data, mainly including spatial databases and attribute databases. Based on public data, this study used the river system map of China to calibrate the generated river network in the study area. The data format and source required by the SWAT model used in this study are shown in Table 1.

[0032] Data Types Format Get the source Digital Elevation Map (DEM) grid or shp Geospatial Data Cloud Administrative boundary map of JH city grid or shp Institute of Geography, Chinese Academy of Sciences Soil type map of JH city grid or shp Institute of Geography, Chinese Academy of Sciences Land use map of JH city grid or shp Institute of Geography, Chinese Academy of Sciences Soil characteristics dbf or txt FAO Meteorological data dbf or txt China Meteorological Information Network Water quality information dbf or txt Hydrological stations in the study area

[0033] Table 1 Sources of data required for SWAT

[0034] The attribute database mainly includes soil attribute database, land use attribute database and meteorological data database.

[0035] For the soil property database, this paper uses the Harmonized World Soil Database version 1.1 (HWSD) jointly created by FAO and IIASA. The soil data currently used in China is the second survey result provided by Nanjing Soil, with a scale of 1:1 million, a grid format, a projection of WGS84, and mainly based on FAO.90 as the classification system. By consulting the soil particle size classification principles in the HWSD manual, it can be seen that HWSD uses the same principles as the United States, that is, the particle size ranges of clay, silt, and sand are 0-0.002mm, 0.002-0.05mm, and 0.05-2mm, so there is no need to perform particle size conversion. The error caused by particle size conversion is reduced, and the difficulty of establishing a database is greatly reduced. This is a method worth promoting. The parameters of the soil database required by the model and the parameters in HWSD are shown in Table 2.

[0036]

[0037] Table 2 SWAT model and HWSD parameter comparison table

[0038] The land use attribute database can reflect the land use status and the coverage and distribution of various vegetation. It will also have a great impact on the output and distribution of nutrients. It is one of the important data in model simulation. Different land use types will affect different runoff processes. Therefore, this study uses the land use data of recent years. The file used to store land use data in the model is crop.dat. The main variables and their definitions are shown in Table 3.

[0039] Parameter name Parameter meaning CPNM 4-letter land cover / vegetation code IDC Land cover / vegetation classification BIO-E Radiation utilization efficiency or bioenergy ratio HVSTI Harvest Index for Optimal Growing Conditions BLAI Maximum possible leaf area index LAIMX1 The maximum leaf area index corresponding to the first point on the optimum leaf area curve LAIMX2 The maximum leaf area index corresponding to the second point on the optimum leaf area curve DLAI The proportion of the season in which vegetation stops growing CHTMX Maximum crown height RDMX Maximum root depth T-OPT Optimal temperature for vegetation growth T-BASE Minimum temperature for vegetation growth WY The lower limit of the harvest index, generally between 0 and HVSTI USLE-C Land cover erodibility factor GSI Maximum stomatal conductivity at high solar radiation and low vapor pressure deficit FRGMAX The vapor pressure difference corresponding to the second point on the pore conductivity curve

[0040] Table 3 SWAT model land use parameters

[0041] Meteorological factors are very important in the hydrological cycle. Meteorological data is the driving force for simulating other processes. The SWAT model requires a large amount of relevant meteorological data, mainly including rainfall, solar radiation, maximum / minimum temperature, wind speed and relative humidity in the basin. Continuous daily rainfall, daily temperature and other climate data have a significant impact on the simulation effect of the model. Parameters such as rainfall, average temperature and solar radiation play an important role in hydrological processes, crop growth and nutrient degradation and transformation. These data can be obtained through the measured values ​​of meteorological stations, but due to the small number of stations and lack of data, the measured values ​​sometimes cannot meet the requirements of the model. Therefore, in order to simulate the climate, a random model for generating weather can be created, that is, a weather generator. The model comes with a weather generator for generating climate data, but the simulation database that comes with the simulation is matched with the United States, so a weather generator suitable for application in the study area should be created. When building a weather generator, a long series of monthly average meteorological statistical data is required, mainly including monthly average fish fall, skewness coefficient, monthly average temperature, dew point temperature and other related parameters.

[0042] For the spatial database, in order to meet the needs of the SWAT model, in the model simulation, all electronic digital maps in the spatial data involved in this article must have the same coordinate system and projection. Therefore, before the simulation, all maps should be converted into the same coordinate system. The projection work should be implemented in Arctoolbox in GIS. The input data are all Albers equal-area conic projections, and the datum surface is D. Krasovsky.

[0043] Before simulating the SWAT model, the obtained graphics should be processed first. According to the purpose of this study and considering the availability and completeness of the data, the main steps are:

[0044] S21. Outline the study area on the watershed map;

[0045] S22. Use ArcGIS to calibrate the watershed map using longitude and latitude;

[0046] S23. Create a new surface file using ArcCatalog and outline the study area;

[0047] S24. Extract the selected area according to the mask MASK;

[0048] S25. Clip the digital elevation map DEM, soil type map, and land use map in the ArcGIS environment to obtain relevant digital maps of the study area.

[0049] S30.SWAT model runs based on the initial setting parameters to simulate the basin hydrological cycle and material transfer process.

[0050] The division of sub-basins is a key step in model simulation. The model can automatically divide the basin according to the digital elevation map of the study area, terrain features and the extracted river network, and realize the division process of sub-basins. After the river network code is determined, the catchment area within each tributary of the river network is defined as a sub-basin, so the size of the river catchment area is the main factor controlling the number of sub-basins. The smaller the area, the more sub-basins are obtained. On the contrary, when the area becomes larger, the number of sub-basins will decrease. According to previous studies, the number of divided sub-basins has an impact on the simulation accuracy of runoff and pollutants to a certain extent. A large number of sub-basins will bring difficulties to data preparation and calculation and evaluation, while a small number of sub-basins will affect the accuracy of model output. Therefore, it is very necessary to divide reasonable sub-basins for research. When dividing the sub-basins in the study area, this paper uses the model recommended threshold of 2000ha.

[0051] When the study area is a plain area with a large number of river networks, the basin outlet should also be considered when dividing the sub-basin, and the sub-basin outlet should be set at each section based on the actual river conditions.

[0052] S40. Calibration and verification;

[0053] S50. Result analysis.

[0054] Field measurement: Collect the measured data of TP and TN from the national control station in JH City and compare them with the model prediction data. The distribution map of the national control station is as follows: Figure 2 As shown. Figure 3a to Figure 3h As shown, Figure 2 Data comparison and analysis were conducted on eight water quality monitoring stations (JJY, FL, DGQ, HWQ, NJQ, TXZ, YDQ, and TK) in the study. The results showed that the measured data of total nitrogen (TN) and total phosphorus (TP) in the two agricultural planting areas of FL and HWQ were highly consistent with the model simulation data, indicating that the simulation accuracy of the SWAT model in these areas was high; the simulation results of TXZ and YDQ were also basically consistent with the measured data, but due to the large number of rural areas in these two areas, there may be some human interference, resulting in a certain error between the simulated data and the measured data; the measured data of JJY, DGQ, and NJQ stations were quite different from the simulated data, which may be because these stations are closer to urban or industrial areas and are more affected by non-agricultural pollution sources; TK station is located outside the agricultural and urban areas, belonging to the water source or ecological protection area, with relatively few human activities and relatively natural water quality conditions, so the measured data of this station are basically consistent with the simulated data trend.

[0055] Through calibration and verification of the model, the study found that there is a good similarity between the simulated data in the JH city area and the measured data of the national control station. This shows that this model can effectively capture the impact of key processes such as rainfall, fertilization, and crop growth on nitrogen and phosphorus loss, and the data used are highly representative. Therefore, the pollution risk assessment based on this model has high reliability and applicability. At the same time, considering the successful application of this model in similar watersheds, this study further verified the applicability of the model in JH City, providing a solid scientific basis for regional agricultural non-point source pollution risk assessment.

[0056] The above embodiments are only used to explain the concept of the present invention, rather than to limit the protection of the rights of the present invention. Any non-substantial changes to the present invention using this concept shall fall within the protection scope of the present invention.

Claims

1. A method for predicting regional agricultural non-point source risk, characterized in that: The following steps are included: S10.SWAT model construction; S20. Data preparation: obtain various data of the study basin and build the spatial database and attribute database required for the operation of the SWAT model. The various data obtained include meteorological data, DEM elevation, soil data, land use data, precipitation data and runoff data; For spatial databases, all electronic digital maps need to be processed uniformly in terms of coordinate system and projection, including the following processes: S21. For spatial databases, outline the study area on the watershed map; S22. Use ArcGIS to calibrate the watershed map using longitude and latitude; S23. Create a new surface file using ArcCatalog and outline the study area; S24. Extract the selected area according to the mask MASK; S25. Clip the digital elevation map DEM, soil type map, and land use map in ArcGIS environment to obtain relevant digital maps of the study area; S30.SWAT model operation: Run the model based on the initial setting parameters to simulate the basin hydrological cycle and material transport process; S40. Calibration and verification; S50. Result analysis.

2. A method for regional agricultural non-point source risk prediction as claimed in claim 1, characterized in that: For the attribute database, the soil data uses the World Soil Database jointly created by FAO and IIASA.

3. A method for regional agricultural non-point source risk prediction as claimed in claim 1, characterized in that: For the attribute database, the meteorological data therein are generated by a weather generator that is suitable for application in the studied area.

4. A method for regional agricultural non-point source risk prediction as claimed in claim 1, characterized in that: In step S30, the SWAT model simulation process will automatically segment the watershed according to the digital elevation map of the study area, terrain features and the extracted river network to achieve sub-watershed division. When performing sub-watershed division, the model threshold is selected as 2000ha.

5. A method for regional agricultural non-point source risk prediction as claimed in claim 4, characterized in that: When dividing sub-basins, the basin outlet needs to be considered, and the sub-basin outlet should be set at each section based on the actual river conditions.

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