Method for determining key source area of small watershed non-point source pollution

Abstract

The invention provides a small watershed non-point source pollution key source area determination method which can be applied to the technical field of pollutant analysis and the field of environmental science. The small watershed non-point source pollution key source area determination method comprises the following steps: acquiring multiple groups of rainfall runoff process information corresponding to a to-be-investigated geographic area; generating N groups of pollutant output quantity theoretical values based on the multiple groups of rainfall runoff process information and the N groups of land information; generating N groups of pollutant transmission resistance values based on pre-collected N groups of geographic information in one-to-one correspondence with the N geographic grid units; based on the N pollutant output quantity theoretical values and the N pollutant transmission resistance values, generating N pollutant output quantity actual values in one-to-one correspondence with the N geographic grid units; and on the basis of the N pollutant output quantity actual values, screening out at least one target geographic grid unit from the N geographic grid units to serve as a small watershed non-point source pollution key source area.

Description

Technical Field

[0001] The present disclosure relates to the field of pollutant analysis technology and environmental science, and more specifically to a method for determining key source areas of non-point source pollution in a small watershed. Background Art

[0002] In order to effectively manage and control non-point source pollution, it is usually necessary to accurately identify the key source areas of non-point source pollution. The key source areas of non-point source pollution include areas in the watershed that contribute more to the non-point source pollution load. Related methods mainly identify geographical areas such as key source areas of non-point source pollution through models such as mechanism models, empirical models and exponential models.

[0003] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the related technologies: the related methods for identifying geographical areas such as key source areas of non-point source pollution mainly focus on analyzing the output potential of pollution sources, but usually ignore the important influence of precipitation, dynamic characteristics of runoff generation and hydrological connectivity on non-point source pollution processes, resulting in the identification of key source areas being not accurate enough and having low feasibility. In addition, when identifying geographical areas such as key source areas of non-point source pollution through existing mechanism models, empirical models and exponential models, there are problems such as high data requirements, insufficient model accuracy, difficulty in adapting to complex land use types and terrain conditions, and insufficient dynamics. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a method for determining key source areas of non-point source pollution in a small watershed, comprising: obtaining multiple sets of precipitation runoff process information corresponding to a geographical area to be investigated, wherein the multiple sets of precipitation runoff process information correspond to multiple precipitation runoff events occurring in the geographical area to be investigated, and the geographical area to be investigated includes N geographical grid units; based on the multiple sets of precipitation runoff process information and N sets of land information acquired in advance and corresponding one-to-one to the N geographical grid units, generating N sets of theoretical values ​​of pollutant output corresponding one-to-one to the N geographical grid units, wherein the pollutants are generated by precipitation runoff events; based on N sets of geographical information collected in advance and corresponding one-to-one to the N geographical grid units, generating N sets of pollutant transmission resistance values ​​corresponding one-to-one to the N geographical grid units; based on the N theoretical values ​​of pollutant output and the N pollutant transmission resistance values, generating N actual values ​​of pollutant output corresponding one-to-one to the N geographical grid units; based on the N actual values ​​of pollutant output, selecting at least one target geographical grid unit from the N geographical grid units as a key source area of ​​non-point source pollution in the small watershed in the geographical area to be investigated.

[0005] According to an embodiment of the present disclosure, the geographic grid unit corresponds to at least one land use type; the land information includes land use type information and land coverage area corresponding to each land use type.

[0006] According to an embodiment of the present disclosure, each group of theoretical values ​​of pollutant output includes K theoretical values ​​of pollutant output; based on multiple groups of precipitation runoff process information, and N groups of land information acquired in advance and corresponding one-to-one to N geographic grid units, generating N groups of theoretical values ​​of pollutant output corresponding one-to-one to N geographic grid units includes: based on a predetermined precipitation threshold, determining K precipitation types corresponding to the multiple groups of precipitation runoff process information; for each geographic grid unit, based on the K groups of precipitation runoff process information corresponding one-to-one to the K precipitation types, and the land use type information corresponding to the geographic grid unit, calculating the K groups of average pollutant concentration values ​​corresponding one-to-one to the K precipitation types, and calculating the K groups of total runoff depth values ​​corresponding one-to-one to the K precipitation types; for each geographic grid unit, based on the K groups of average pollutant concentration values, the K groups of total runoff depth values, and the land coverage area corresponding to each land use type, calculating the K theoretical values ​​of pollutant output corresponding one-to-one to the K precipitation types.

[0007] According to an embodiment of the present disclosure, land use type information includes: a runoff parameter value corresponding to each land use type; calculating K groups of total runoff depth values ​​corresponding to K precipitation types one by one includes: determining at least one runoff parameter value corresponding to the geographic grid unit based on at least one land use type corresponding to the geographic grid unit; determining at least one accumulated precipitation corresponding to the geographic grid unit based on at least one land use type corresponding to the geographic grid unit; for various precipitation types, calculating the total runoff depth value corresponding to the precipitation type based on at least one runoff parameter value corresponding to the geographic grid unit and at least one accumulated precipitation.

[0008] According to an embodiment of the present disclosure, the geographic information includes: geographic grid unit distance information and geographic characteristic indicators; the geographic grid unit distance information includes: the distance value from the geographic grid unit to the pollutant collection area.

[0009] According to an embodiment of the present disclosure, based on N groups of pre-collected geographic information corresponding one-to-one to N geographic grid cells, generating N groups of pollutant transmission resistance values ​​corresponding one-to-one to N geographic grid cells includes: for each geographic grid cell, determining at least one geographic characteristic indicator corresponding to the geographic grid cell, and determining a weight value corresponding to the at least one geographic characteristic indicator; for each geographic grid cell, based on the distance value from the geographic grid cell to the pollutant accumulation area, and the weight value corresponding to the at least one geographic characteristic indicator, obtaining the pollutant transmission resistance value corresponding to the geographic grid cell.

[0010] According to an embodiment of the present disclosure, the geographical characteristic index includes: a landscape type index, a soil type index, and a geographical information index; the geographical information index includes at least one of the following: an altitude index, a distance index, a slope index, a humidity index, and a vegetation coverage index.

[0011] According to an embodiment of the present disclosure, based on N theoretical values ​​of pollutant outputs and N pollutant transmission resistance values, N actual values ​​of pollutant outputs corresponding one-to-one to N geographic grid units are generated, including: based on N pollutant transmission resistance values, N hydrological connectivity standard values ​​corresponding one-to-one to N geographic grid units are generated; based on N theoretical values ​​of pollutant outputs and N hydrological connectivity standard values, the actual values ​​of pollutant outputs are calculated.

[0012] According to an embodiment of the present disclosure, based on N theoretical values ​​of pollutant output and N standard values ​​of hydrological connectivity, the actual value of pollutant output is calculated, including: for each geographic grid unit, calculating the product of the theoretical value of pollutant output and the standard value of hydrological connectivity corresponding to the geographic grid unit to obtain the actual value of pollutant output.

[0013] According to an embodiment of the present disclosure, based on N actual values ​​of pollutant output, selecting at least one target geographic grid unit from N geographic grid units includes: performing predetermined grading processing on the N actual values ​​of pollutant output to obtain a grading processing result; and selecting at least one target geographic grid unit from the N geographic grid units based on the grading processing result.

[0014] According to the embodiments of the present disclosure, the theoretical value of pollutant output is determined based on the precipitation type and other precipitation runoff process information, and the actual value of pollutant output is determined based on geographic information. The impact of the entire process from the occurrence of precipitation runoff events, the transmission of pollutants through runoff to the influx of pollutants into water bodies on the actual output of pollutants can be comprehensively analyzed, and the impact of various geographic information such as topography, land use, and vegetation coverage can be comprehensively considered. This allows the key source areas of non-point source pollution in small watersheds to be dynamically identified under different rainfall conditions, thereby improving the identification accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0016] Figure 1 A flowchart of a method for determining a key source area of ​​non-point source pollution in a small watershed according to an embodiment of the present disclosure is schematically shown;

[0017] Figure 2 Schematically showing the runoff and pollution analysis results of different land use types according to the embodiment of the present disclosure;

[0018] FIG3 (a), FIG3 (b), and FIG3 (c) schematically illustrate theoretical values ​​of multiple pollutant outputs under different precipitation types according to an embodiment of the present disclosure;

[0019] Figure 4A schematic diagram schematically shows the landscape types included in different land use types according to an embodiment of the present disclosure;

[0020] FIG5(a), FIG5(b), and FIG5(c) schematically illustrate schematic diagrams of hydrological connectivity standard values ​​under different precipitation types according to an embodiment of the present disclosure;

[0021] FIG6( a ), FIG6 ( b ), and FIG6 ( c ) schematically illustrate actual values ​​of pollutant output under different precipitation types according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0023] The endpoints and any values ​​of the ranges disclosed in this disclosure are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of each range, the endpoint values ​​of each range and the individual point values, and the individual point values ​​can be combined with each other to obtain one or more new numerical ranges, and these numerical ranges should be regarded as specifically disclosed in this disclosure.

[0024] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components. All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0025] In the process of realizing the present disclosure, it is found that the identification method of key source areas of relevant non-point source pollution mainly focuses on analyzing the output potential of pollution sources, but usually ignores the important influence of precipitation, dynamic characteristics of runoff production process and hydrological connectivity on non-point source pollution process, resulting in the identification of key source areas is not accurate enough and the feasibility is low. And the identification technology of relevant non-point source pollution mainly relies on mechanism model, empirical model and index model. The mechanism model requires a lot of measured data support and has a high dependence on data; while the empirical and index models are difficult to adapt to the spatiotemporal changes of basin characteristics due to parameter simplification, resulting in limited identification accuracy and applicability. Specifically, although the mechanism model can accurately simulate hydrological processes and pollutant transport, it has a strong dependence on high-resolution and long-time series data, and the calculation is complex, which makes it difficult to be widely used in small watersheds with scarce data. The empirical model usually only simply estimates the pollution load through empirical formulas. Due to the reliance on fixed parameters, it is difficult to reflect the pollutant output under complex landscape structures and dynamic hydrological conditions, resulting in low identification accuracy. Although the index model is suitable for risk assessment of specific pollutants, it is mostly limited to a single pollutant and lacks a comprehensive analysis of multiple pollutants and diversified land use. In addition, related models generally have the problem of insufficient dynamics and are unable to accurately capture the spatiotemporal changes in pollutant output during precipitation and runoff. For example, differences in precipitation intensity, duration, precipitation amount and other factors will cause changes in the distribution and migration patterns of pollutants in time and space, and related models are usually unable to capture such spatiotemporal changes. Under complex landscape conditions, the spatial accuracy of identifying key source areas is low; at the same time, although some methods take into account hydrological connectivity, the simulation of pollutant transmission paths is not accurate enough, and the influence of factors such as terrain and vegetation on the resistance of pollutant transmission is ignored. In view of this, an embodiment of the present disclosure provides a method for determining key source areas of non-point source pollution in a small watershed.

[0026] Figure 1 The flowchart of the method for determining the key source area of ​​non-point source pollution in a small watershed according to an embodiment of the present disclosure is schematically shown. The method for determining the key source area of ​​non-point source pollution in a small watershed in this embodiment includes operations S110 to S150.

[0027] In operation S110, multiple sets of precipitation-runoff process information corresponding to the geographic area to be investigated are obtained, wherein the multiple sets of precipitation-runoff process information correspond to multiple precipitation runoff events occurring in the geographic area to be investigated, and the geographic area to be investigated includes N geographic grid units.

[0028] According to the embodiments of the present disclosure, pollutants can enter the environment such as soil through runoff and other means under the influence of natural conditions such as rainfall and snowfall, generating non-point source pollution. For example, pollutants can flow through different geographical areas on the ground through runoff formed by rainfall, and finally enter the river. Due to different precipitation conditions and the different natural conditions of the geographical areas through which the runoff flows, the non-point source pollution load contribution of different geographical areas is different. The geographical area that contributes more to the non-point source pollution load in the geographical area through which the runoff flows can be regarded as the key source area of ​​non-point source pollution.

[0029] According to an embodiment of the present disclosure, in operation S110, the geographic area to be investigated includes multiple geographic areas through which runoff flows. In order to accurately determine the key source areas of non-point source pollution in the geographic area to be investigated, the geographic area to be investigated can be divided into N geographic grid units in advance, and the non-point source pollution load contribution of each geographic grid unit is evaluated respectively, so as to screen out the geographic areas with larger non-point source pollution load contribution from the N geographic grid units, and obtain the key source areas of non-point source pollution.

[0030] The geographic area to be investigated can be divided into N geographic grid units using Geographic Information System (GIS) technology. The basic geographic data of the N geographic grid units can be collected in advance, including but not limited to digital elevation models, land use / vegetation cover maps, soil type distribution maps, vegetation index images, etc., and the time consistency and quality reliability of the basic geographic data can be ensured in advance.

[0031] According to the embodiments of the present disclosure, by dividing the geographic area to be investigated into N geographic grid units in advance and analyzing the surface source pollution load contributions of the N geographic grid units separately, rather than directly analyzing the entire geographic area to be investigated, it is possible to comprehensively consider the computing efficiency and spatial resolution requirements and improve the computing efficiency and accuracy.

[0032] According to the embodiments of the present disclosure, multiple precipitation runoff events occurring in the geographical area to be investigated within a predetermined time period can be determined. Precipitation runoff events can include, for example, events that generate surface runoff through rainfall, snowfall, and other precipitation, and precipitation runoff process information corresponding to different land use types in the area to be investigated and each precipitation runoff event can be collected separately. The precipitation runoff process information includes, for example, precipitation information such as precipitation amount, precipitation duration, and precipitation intensity, as well as runoff information such as runoff time, runoff rate, flow rate, and pollutant concentration at each runoff moment.

[0033] According to the embodiments of the present disclosure, the entire pollutant transmission process includes pollutants reaching the ground through precipitation, flowing through different geographical areas on the ground through runoff, and merging into receiving water bodies such as rivers and lakes through runoff. The load of pollutants that eventually enter the receiving water body is affected by precipitation conditions and the geographical conditions of the geographic grid unit. Specifically, for each geographic grid unit, the corresponding precipitation conditions and geographical conditions will affect the non-point source pollutant output potential of the geographic grid unit, and the geographical conditions will also affect the resistance of the output pollutants to the transmission through runoff. The non-point source pollutant output potential and the resistance to the transmission of pollutants through runoff can jointly affect the non-point source pollution load contribution of each geographic grid unit.

[0034] In operation S120, based on multiple sets of precipitation runoff process information and N sets of pre-acquired land information corresponding one-to-one to N geographic grid cells, N sets of theoretical values ​​of pollutant output corresponding one-to-one to N geographic grid cells are generated, wherein the pollutants are generated by precipitation runoff events.

[0035] According to an embodiment of the present disclosure, the theoretical value of pollutant output can characterize the non-point source pollutant output potential, which can represent the maximum amount of pollutants that a geographic grid unit may release into the environment. The precipitation runoff process information may include the accumulated rainfall. The land information may include the land use type corresponding to the geographic grid unit, and the land use type may include woodland, garden, road, etc., and may also include other land use types, which are not limited here.

[0036] Both the precipitation runoff process information and geographic information will have an impact on the theoretical value of pollutant output. Specifically, the cumulative rainfall required for different land use types to form runoff may be different. For example, roads may start to produce runoff earlier due to more impervious surfaces, while forests may start to produce runoff later due to good soil permeability. In the case where the precipitation runoff event is rainfall, the pollutant content corresponding to different rainfall types may be different. For example, since heavy rain has more precipitation, it carries more pollutants and its corresponding pollutant concentration may be greater. For each geographic grid unit, the land use type included in the geographic grid unit can be determined, and the rainfall type corresponding to the geographic grid unit can be determined. The rainfall type can include light rain, moderate rain, and heavy rain. The theoretical value of the pollutant output corresponding to each geographic grid unit can be calculated based on the precipitation runoff process information and land information corresponding to each geographic grid unit.

[0037] According to an embodiment of the present disclosure, for each geographic grid unit, which corresponds to at least one rainfall type and at least one land use type, the theoretical value of pollutant output corresponding to each rainfall type can be calculated to obtain a set of theoretical values ​​of pollutant output corresponding to the geographic grid unit. Specifically, the theoretical value of pollutant output corresponding to each rainfall type can be calculated based on the average concentration of pollutants in the geographic grid unit corresponding to each rainfall type, the total runoff depth corresponding to different land use types within a predetermined time period, and the land cover area corresponding to each land use type.

[0038] In operation S130, N groups of pollutant transport resistance values ​​corresponding to the N geographic grid cells in a one-to-one manner are generated based on N groups of pre-collected geographic information corresponding to the N geographic grid cells in a one-to-one manner.

[0039] According to an embodiment of the present disclosure, the pollutant transmission resistance value can characterize the resistance encountered by pollutants during transmission. The hydrological connectivity of the geographic grid unit can be evaluated based on the pollutant transmission resistance value, and the hydrological connectivity can characterize the convenience of pollutant transmission through runoff. Geographic information may include, for example, landscape morphology, soil type, etc. Geographic information will affect the pollutant transmission resistance value of the geographic grid unit. For example, for different soil types, the hydrological connectivity of yellow soil is poor, while the hydrological connectivity of alluvial soil is better. The resistance coefficient of each geographic grid unit can be determined based on geographic information such as landscape morphology, soil type, and vegetation cover type, and the pollutant transmission resistance value of each geographic grid unit can be calculated based on the resistance coefficient.

[0040] In operation S140 , N actual values ​​of pollutant output corresponding to N geographic grid units are generated based on the N theoretical values ​​of pollutant output and the N values ​​of pollutant transmission resistance.

[0041] According to an embodiment of the present disclosure, for each geographic grid unit, an actual value of pollutant output corresponding to the geographic grid unit can be generated based on the theoretical value of pollutant output and the pollutant transmission resistance value of the geographic grid unit. The actual value of pollutant output is used to characterize the contribution of non-point source pollution load. The larger the actual value of pollutant output, the higher the contribution of non-point source pollution load.

[0042] In operation S150, based on the actual values ​​of the N pollutant outputs, at least one target geographic grid unit is selected from the N geographic grid units as a key source area of ​​non-point source pollution in a small watershed in the geographic area to be investigated.

[0043] According to an embodiment of the present disclosure, after determining the actual value of the pollutant output of each geographic grid unit, the geographic grid unit with a larger actual value of the pollutant output can be used as a key source area for non-point source pollution in a small watershed. The key source area for non-point source pollution in a small watershed is used to characterize the geographical area to be investigated that needs to be focused on or governed.

[0044] According to the embodiments of the present disclosure, by calculating the theoretical value of pollutant output and the actual value of pollutant output of each geographic grid unit to obtain the actual value of pollutant output, the influence of the whole process from the occurrence of precipitation runoff events, the transmission of pollutants through runoff, and the influx of pollutants into the water body on the actual output of pollutants can be comprehensively analyzed, so that the key source area of ​​non-point source pollution in small watersheds can be accurately determined. The theoretical value of pollutant output is determined based on precipitation type and other precipitation runoff process information, and the pollutant output characteristics of different rainfall types can be determined, so that the pollution load under different rainfall conditions can be dynamically evaluated, which solves the problem that the relevant methods ignore the dynamic nature of rainfall. By comprehensively considering the influence of various geographic information such as topography, land use, and vegetation coverage, the transmission path and resistance of pollutants from the source to the water body can be simulated, so as to fully consider the influence of hydrological connectivity on pollutant output, thereby improving the accuracy of the description of the pollutant transmission process under complex landscape structures. By determining the actual value of pollutant output based on the theoretical value of pollutant output and the actual value of pollutant output at the same time, the key source area of ​​non-point source pollution in small watersheds can be dynamically identified under different rainfall conditions, and the recognition accuracy of the key source area determination process of non-point source pollution in small watersheds in space and time is improved.

[0045] According to the embodiments of the present disclosure, when determining the non-point source pollution source area in a small watershed through a related mechanism model, the related mechanism model usually requires a large amount of data to output accurate results. Since the watershed is small, its related data is also less. Therefore, the accuracy is low when the related model is applied to identify the key source area of ​​non-point source pollution in the small watershed. Compared with the above-mentioned related model, by combining geographic information system technology and short-term rainfall observations, the difficulty of data acquisition can be reduced and the data acquisition requirements can be simplified, so that it can be applicable to small watersheds with limited data. In addition, since the method for determining the key source area of ​​non-point source pollution in a small watershed of the embodiment of the present disclosure takes into account the influence of precipitation runoff events and hydrological connectivity at the same time, it can more accurately and dynamically identify the key source area of ​​non-point source pollution in the small watershed, thereby improving the accuracy and efficiency of pollution control.

[0046] According to an embodiment of the present disclosure, the geographic grid unit corresponds to at least one land use type. The land information includes land use type information and land coverage area corresponding to each land use type.

[0047] According to an embodiment of the present disclosure, each group of pollutant output theoretical values ​​includes K pollutant output theoretical values. Based on multiple groups of precipitation runoff process information and N groups of land information obtained in advance and corresponding to N geographic grid units, generating N groups of pollutant output theoretical values ​​corresponding to N geographic grid units includes operations 11 to 13.

[0048] Operation 11: determining K precipitation types corresponding to multiple sets of precipitation runoff process information based on a predetermined precipitation threshold.

[0049] According to an embodiment of the present disclosure, the predetermined precipitation threshold may be pre-set. Precipitation types may include light rain, moderate rain, and heavy rain, wherein the rainfall thresholds corresponding to light rain, moderate rain, and heavy rain may be determined based on the accumulated rainfall corresponding to the start of runoff generation for different land use types. Daily precipitation data for the geographic area to be investigated within a predetermined time period and surface runoff data corresponding to rainfall events of different land use types in the area to be investigated may be collected in advance, and the daily precipitation data may include, for example, daily accumulated precipitation, precipitation type, duration of each precipitation event, precipitation intensity, etc.; surface runoff data may include, for example, runoff volume, runoff rate, pollutant concentration, etc. corresponding to every 5 minutes, 10 minutes, and 30 minutes during rainfall, as well as flow.

[0050] Operation 12: For each geographic grid unit, based on K groups of precipitation-runoff process information corresponding to K types of precipitation types and land use type information corresponding to the geographic grid unit, calculate K groups of average pollutant concentration values ​​corresponding to K types of precipitation types, and calculate K groups of total runoff depth values ​​corresponding to K types of precipitation types.

[0051] According to the embodiments of the present disclosure, for the same geographic grid unit, the theoretical values ​​of pollutant output corresponding to different precipitation types may be different. The average concentration of pollutants and the total runoff depth value corresponding to various precipitation types can be calculated respectively to obtain the theoretical values ​​of pollutant output corresponding to various precipitation types. For the same precipitation runoff event, the cumulative rainfall corresponding to the start of runoff and pollution production of different land use types can be determined, and the precipitation duration, precipitation intensity and other information can also be determined to obtain multiple sets of precipitation runoff process information. Multiple sets of precipitation runoff process information can characterize the temporal characteristics of runoff and pollution production of different land use types.

[0052] According to an embodiment of the present disclosure, for a geographic grid unit, under the same precipitation type, the average pollutant concentrations and total runoff depth values ​​corresponding to different land use types are also different. The influence of land use types can be comprehensively considered based on the rainfall event average concentration model (EMC model) to calculate the average pollutant concentrations of different land use types under different precipitation types. For example, the average pollutant concentrations corresponding to different land use types under different precipitation types can be calculated by the following formula (1).

[0053] (1)

[0054] Among them, M is the mass of pollutants, V is the total runoff, C is the pollutant concentration generated by the precipitation runoff event, Q is the flow rate, t is the time interval, and EMC represents the average concentration of pollutants.

[0055] Operation 13: For each geographic grid unit, based on K groups of average pollutant concentration values, K groups of total runoff depth values, and the land cover area corresponding to each land use type, calculate K theoretical values ​​of pollutant output corresponding to K precipitation types.

[0056] According to an embodiment of the present disclosure, the theoretical value of pollutant output corresponding to each precipitation type can be calculated by the following formula (2).

[0057] (2)

[0058] Among them, L is the theoretical value of pollutant output of each geographic grid unit, n is the nth land use type, A is the land cover area, and R is the total runoff depth value.

[0059] According to the embodiments of the present disclosure, by calculating the average pollutant concentrations corresponding to different land use types under different precipitation types, and the total runoff depth corresponding to each land use type, it is possible to consider both the impact of precipitation factors on the pollutant output of geographic grid units and the impact of land use types on pollutant output. This can more accurately and quantitatively describe the law between the total runoff depth value and the pollutant output, thereby improving the accuracy of the calculation results of the theoretical value of the pollutant output.

[0060] According to an embodiment of the present disclosure, the land use type information includes: a runoff parameter value corresponding to each land use type. Calculating K groups of runoff depth values ​​corresponding to K precipitation types one by one includes operations 21 to 23.

[0061] Operation 21: Determine at least one runoff parameter value corresponding to the geographic grid cell based on at least one land use type corresponding to the geographic grid cell.

[0062] According to an embodiment of the present disclosure, runoff parameter values ​​include, for example, initial loss rainfall value, infiltration rainfall value, loss coefficient, and dimensionless curve number, wherein the source of the dimensionless curve number corresponding to each land use type includes, but is not limited to, empirical parameters, literature references, and database searches. Initial loss rainfall value, infiltration rainfall value, loss coefficient, and dimensionless curve number can also be searched through the relevant recommendation table of the runoff curve number model (SCS-CN model) based on the accumulated rainfall corresponding to the runoff generated by different land use types.

[0063] Operation 22: Determine at least one accumulated precipitation corresponding to the geographic grid cell based on at least one land use type corresponding to the geographic grid cell.

[0064] According to an embodiment of the present disclosure, the cumulative rainfall of different rainfall types in the geographic area to be investigated within a predetermined time period can be calculated based on different rainfall types and the distribution frequency of rainfall events. For example, the cumulative daily rainfall data of the geographic area to be investigated can be calculated.

[0065] Operation 23: For each precipitation type, based on at least one runoff parameter value corresponding to the geographic grid unit and at least one accumulated precipitation, calculate a total runoff depth value corresponding to the precipitation type.

[0066] According to the embodiments of the present disclosure, the total runoff depth values ​​corresponding to different land use types under different precipitation types can be calculated by the following formula (3), the initial loss rainfall can also be calculated by the following formula (4), and the infiltration rainfall can also be calculated by the following formula (5).

[0067] (3)

[0068] (4)

[0069] (5)

[0070] Among them, R is the total runoff depth value, P is the cumulative precipitation, is the initial loss rainfall, S is the infiltration rainfall, λ is the loss coefficient, and CN is the dimensionless curve number.

[0071] According to an embodiment of the present disclosure, by generating a total runoff depth value based on both the accumulated precipitation and the runoff parameter value, comprehensive factors affecting runoff generation can be considered, thereby improving the calculation accuracy of the total runoff depth value.

[0072] According to an embodiment of the present disclosure, the geographic information includes: geographic grid unit distance information and geographic characteristic indicators; the geographic grid unit distance information includes: the distance value from the geographic grid unit to the pollutant collection area.

[0073] According to an embodiment of the present disclosure, a pollutant collection area may be a water body into which pollutants are finally collected when they are transported through runoff. For example, if pollutants finally flow into a river through runoff, the river may be a pollutant collection area. The geographic characteristic index is used to characterize the landscape type, soil type, and geographic information of each geographic grid unit.

[0074] According to an embodiment of the present disclosure, based on N sets of pre-collected geographic information corresponding to N geographic grid units, generating N sets of pollutant transmission resistance values ​​corresponding to N geographic grid units includes: for each geographic grid unit, determining at least one geographic characteristic index corresponding to the geographic grid unit, and determining a weight value corresponding to at least one geographic characteristic index; for each geographic grid unit, based on the distance value from the geographic grid unit to the pollutant collection area, and the weight value corresponding to at least one geographic characteristic index, obtaining the pollutant transmission resistance value corresponding to the geographic grid unit. The pollutant transmission resistance value can characterize the efficiency of pollutant transmission from each geographic unit grid to the receiving water body, and can also determine the path of pollutant transmission from each geographic unit grid to the receiving water body based on the pollutant transmission resistance value.

[0075] According to an embodiment of the present disclosure, determining a weight value corresponding to at least one geographic characteristic indicator may be: determining the weight value based on geographical factors such as landscape type, soil type, terrain, vegetation coverage, etc. of each geographic grid unit. Specifically, the weight value may be determined based on the magnitude of the effect of each geographic characteristic indicator on hydrological connectivity, and the greater the barrier effect of each geographic characteristic indicator on pollutant transmission, the higher the weight value.

[0076] According to an embodiment of the present disclosure, the determined weight values ​​can be connected to a pre-constructed weight table corresponding to each geographic grid unit through geographic information system software. The weight table may include the geographic characteristic indicators and their weight values ​​included in each geographic grid unit. And different resistance surfaces can be established based on geographic characteristic indicators such as landscape type, soil type, geographic information, etc. through geographic information system software, and the pollutant transmission resistance value can be calculated through the Minimum Cumulative Resistance Model (MCR). Among them, the geographic information system software can be, for example, software used for map making and spatial data analysis. Specifically, the pollutant transmission resistance value calculated by the MCR model can be calculated by the following formula (6).

[0077] (6)

[0078] Among them, MCR is the pollutant transmission resistance value, which can characterize the minimum resistance value that pollutants need to overcome from the geographical grid unit where the precipitation runoff event occurs, through different types of landscapes, soil types, and different geographical factors, to reach the pollutant collection area. D is the distance value from the pollutant collection area j to each geographical grid unit i, R i is a weight value corresponding to at least one geographic characteristic indicator.

[0079] According to the embodiments of the present disclosure, by calculating the pollutant transmission resistance value of each geographic grid unit, the hydrological connectivity of each geographic grid unit can be determined. When the pollutant transmission resistance is small, the hydrological connectivity is usually good, and the enhancement of hydrological connectivity is conducive to the transmission of pollutants. By comprehensively considering the hindering effect of various geographic characteristic indicators such as landscape type, soil type, and geographic information on pollutant transmission, the transmission path and resistance of pollutants from the source to the water body can be simulated, and the accuracy of describing the pollutant transmission process under complex landscape structures can be improved, so that the pollutant transmission resistance value can be determined more accurately.

[0080] According to an embodiment of the present disclosure, specifically, the geographical characteristic indicators include: landscape type indicators, soil type indicators, and geographical information indicators; the geographical information indicators include at least one of the following: altitude indicators, distance indicators, slope indicators, humidity indicators, and vegetation coverage indicators.

[0081] According to an embodiment of the present disclosure, the soil types included in each geographic grid unit include, for example, red soil, dark brown soil, etc. The landscape type included in each geographic grid unit can be determined by landscape morphology spatial pattern analysis (MAPA), and the landscape type index can include at least one landscape morphology, and each landscape morphology includes but is not limited to a core area, an island, etc. Each landscape morphology and its definition are shown in Table 1.

[0082] Table 1

[0083]

[0084] According to the embodiments of the present disclosure, for landscape morphological indicators, the resistance value between the target patch and other landscapes can be defined based on the morphological attributes such as the area size, boundary characteristics and connectivity of each landscape unit, and the weight value can be determined based on the material transfer capacity of the landscape unit in the ecological process. For example, the core area with a larger area has the smallest weight because it is conducive to material exchange, and its weight can be, for example, 0.2, while the isolated patch with a smaller scale has the highest weight due to limited material transfer, and its weight can be, for example, 1.0. For soil type indicators, the soil erosion coefficient value can be determined based on existing data, and the weight value can be determined based on the soil erosion coefficient value. For indicators such as altitude, distance, slope, etc. in geographic information indicators, each of the indicators can be divided into 5 levels by the natural breakpoint method, and the weight value can be determined based on the level. For humidity indicators, its weight is set to 0 or 1 according to whether it generates runoff. The weight value of the vegetation coverage indicator can be determined according to the indicator coverage percentage.

[0085] According to the embodiments of the present disclosure, by combining the average concentration model of rainfall events, the landscape morphology spatial pattern analysis and the minimum cumulative resistance model, various factors such as topography, land use, vegetation coverage, etc. can be comprehensively considered to simulate the transmission path and resistance of pollutants from the source to the water body, thereby improving the accuracy of the description of the pollutant transmission process under complex landscape structures.

[0086] Non-point source pollution in small watersheds is characterized by wide distribution, spatial variability, randomness and delayed effects. For example, spatial variability is reflected in the fact that the pollution loads corresponding to different land use types may be different. Randomness and wide distribution are reflected in the random occurrence of precipitation runoff events in time and space respectively. The delayed effect is reflected in the fact that pollutants will not have an immediate impact on the water body, but will be gradually released into the water body after a certain period of time through processes such as runoff and soil erosion.

[0087] However, the relevant static models often have difficulty in capturing the above characteristics, such as the spatiotemporal dynamics of pollutant generation and transport under different rainfall conditions, which limits their effectiveness in precise watershed management and leads to low accuracy in identifying key source areas of non-point source pollution in small watersheds. In addition, it is difficult for relevant methods to achieve integrated application of rainfall event average concentration model, landscape morphological spatial pattern analysis and minimum cumulative resistance model. Specifically, the event average concentration application model requires high-precision rainfall data support, and existing observation methods are difficult to meet the needs of refined simulation at the scale of small watersheds; morphological spatial pattern analysis lacks the theoretical basis for transformation for pollutant transport analysis; and the coupling of minimum cumulative resistance and morphological spatial pattern analysis involves complex scale conversion and parameter matching problems.

[0088] According to the embodiment of the present disclosure, by generating the theoretical value of pollutant output and the pollutant transmission resistance value corresponding to the geographic grid unit, and generating the actual value of pollutant output based on the theoretical value of pollutant output and the pollutant transmission resistance value, an integrated framework combining the average concentration model of rainfall events, landscape morphological spatial pattern analysis and minimum cumulative resistance model can be formed, so that the critical source areas (Critical Source Areas, abbreviated as CSAs) of small watershed non-point source pollution can be dynamically and accurately identified, providing a relatively accurate and adaptable tool for pollution control and precise watershed management of specific rainfall, which is conducive to the subsequent improvement of water quality in the critical source areas of small watershed non-point source pollution. Compared with the relevant static model, a theoretical framework for agricultural pollution source identification based on morphological spatial pattern analysis is constructed, which can dynamically capture the spatiotemporal variability of pollutant generation and transmission, improve the accuracy of the average concentration model of rainfall events at the small watershed scale, and construct a minimum cumulative resistance model-morphological spatial pattern analysis parameter transfer algorithm, so that dynamic simulation of pollutants in agricultural small watersheds can be performed.

[0089] According to an embodiment of the present disclosure, based on N theoretical values ​​of pollutant outputs and N pollutant transmission resistance values, N actual values ​​of pollutant outputs corresponding one-to-one to N geographic grid units are generated, including: based on N pollutant transmission resistance values, N hydrological connectivity standard values ​​corresponding one-to-one to N geographic grid units are generated; based on N theoretical values ​​of pollutant outputs and N hydrological connectivity standard values, the actual values ​​of pollutant outputs are calculated.

[0090] According to an embodiment of the present disclosure, based on N pollutant transmission resistance values, generating N hydrological connectivity standard values ​​corresponding to N geographic grid units includes: for each pollutant transmission resistance value, calculating the reciprocal value of the pollutant transmission resistance value to obtain the hydrological connectivity initial value; and normalizing the hydrological connectivity initial value to obtain the hydrological connectivity standard value. The hydrological connectivity initial value can be calculated by the following formula (7), and the hydrological connectivity standard value can be calculated by the following formula (8).

[0091] (7)

[0092] (8)

[0093] Among them, MCR is the pollutant transmission resistance value, CI is the initial value of hydrological connectivity, and CI min , CI max are the minimum and maximum values ​​of the N initial values ​​of hydrological connectivity, respectively. CI1 is the standard value of hydrological connectivity, and the range of the standard value of hydrological connectivity is between 0 and 1.

[0094] According to an embodiment of the present disclosure, based on N theoretical values ​​of pollutant output and N standard values ​​of hydrological connectivity, calculating the actual value of pollutant output includes: for each geographic grid unit, calculating the product of the theoretical value of pollutant output and the standard value of hydrological connectivity corresponding to the geographic grid unit to obtain the actual value of pollutant output. The actual value of pollutant output can be calculated by the following formula (9).

[0095] (9)

[0096] Among them, Pr is the actual value of pollutant output of each geographic grid unit, L is the theoretical value of pollutant output corresponding to the geographic grid unit, and CI1 is the standard value of hydrological connectivity corresponding to the geographic grid unit.

[0097] According to the embodiments of the present disclosure, by analyzing the source-flow-sink characteristics of small watershed surface source pollution based on the full chain of rainfall process, by introducing the average concentration model of rainfall events, the spatial pattern analysis of landscape morphology and the minimum cumulative resistance model, running through GIS data and short-term rainfall observation data, considering physical distance and terrain, comprehensive land use and vegetation coverage and other factors, combining potential pollution load and hydrological connectivity, accurately simulating the pollutant transmission path, according to different rainfall events, it is possible to dynamically identify pollutant output characteristics and key source areas in different rainfall events and time periods, more accurately reflect the pollution changes driven by rainfall than traditional methods, reduce dependence on long time series data, significantly improve the recognition accuracy under complex landscape conditions, suitable for complex small watersheds with mosaic distribution of agriculture, residential and natural land, and can effectively cope with the complex structure of coexistence of multiple land types, avoiding the shortcomings under complex land use conditions, and has high practicality.

[0098] According to an embodiment of the present disclosure, based on N actual values ​​of pollutant output, selecting at least one target geographic grid unit from N geographic grid units includes: performing predetermined grading processing on the N actual values ​​of pollutant output to obtain a grading processing result; and selecting at least one target geographic grid unit from the N geographic grid units based on the grading processing result.

[0099] According to an embodiment of the present disclosure, the predetermined hierarchical processing includes, for example, performing natural breakpoint processing on N actual values ​​of pollutant outputs to obtain actual values ​​of pollutant outputs of multiple categories. Screening out at least one target geographic grid unit from N geographic grid units may, for example, be to use the actual value of pollutant output of a predetermined category as the target actual value of pollutant output, determine the geographic grid unit corresponding to the target actual value of pollutant output, and obtain the target geographic grid unit. For example, the natural breakpoint processing may be performed using geographic information system software to obtain the actual values ​​of pollutant outputs of five categories, and the actual values ​​of pollutant outputs of categories 4 and 5 may be used as the target actual values ​​of pollutant outputs. The actual values ​​of pollutant outputs of categories 4 and 5 are higher in the N actual values ​​of pollutant outputs. Based on the predetermined hierarchical processing, the target grid unit is screened out from multiple geographic grid units, and the key source areas of non-point source pollution in small watersheds can be determined more accurately.

[0100] The disclosure is further described below by embodiments and related test experiments. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the disclosed embodiments. However, it is apparent that one or more embodiments may also be implemented without these specific details. Moreover, in the absence of conflict, the details in the following embodiments may be arbitrarily combined into other feasible embodiments.

[0101] Example 1

[0102] In this embodiment, a reservoir is used as a small watershed to determine the key source area of ​​non-point source pollution in the small watershed within the reservoir, wherein the key source area of ​​non-point source pollution in the small watershed can be the key source area of ​​non-point source pollution in the reservoir. Specifically, the rainfall data and runoff observation data of the reservoir area in 20XX can be collected, and the non-point source pollutant load under different rainfall events can be calculated using the average concentration model of rainfall events. The hydrological connectivity of the reservoir area can be analyzed in combination with the landscape morphological spatial pattern analysis and the minimum cumulative resistance model, so as to determine the key source area of ​​non-point source pollution in the small watershed. Based on the geographic information system technology, the reservoir area can be subdivided into geographic unit grids with a size of 30m×30m, and the geographic attribute, soil attribute, vegetation coverage rate and other geographic characteristic indicators of each geographic unit grid can be assigned weight values ​​based on the digital elevation model (DEM), land use type, soil type, vegetation coverage and other data, so as to facilitate subsequent analysis. The precipitation runoff process information of different land use types in each geographic grid unit within a predetermined time period is collected to generate a rainfall information collection table for the study area. Land use types include, for example, land for road traffic, residential land, cultivated land, garden land, forest land, etc. The rainfall information collection table of the study area is shown in Table 2.

[0103] Table 2

[0104]

[0105] According to the embodiments of the present disclosure, for the same precipitation runoff event, the runoff and pollution generation processes of different land use types can be analyzed. Figure 2 The following diagram schematically shows the analysis results of runoff and pollution generation of different land use types according to the embodiment of the present disclosure. The pollutant may be phosphorus, for example. Figure 2 As shown in the figure, different land use types correspond to different total phosphorus concentrations and total phosphorus loads.

[0106] According to the embodiment of the present disclosure, the predetermined precipitation threshold is determined in combination with the meteorological and hydrological data of the reservoir area and the management measures of the monitoring station, and the rainfall events occurring in the reservoir area within the predetermined time period are divided into multiple rainfall types based on the predetermined precipitation threshold, and the rainfall types include, for example, light rain, moderate rain, and heavy rain, wherein 11.4mm> light rain rainfall ≥ 1.4mm, 24mm> moderate rain rainfall ≥ 11.4mm, and heavy rain rainfall ≥ 24mm, and for each geographic grid unit, the average concentration values ​​of pollutants of different land use types under different rainfall types are calculated. The average concentration values ​​of pollutants of different land use types under different rainfall types are shown in Table 3.

[0107] Table 3

[0108]

[0109] According to the embodiments of the present disclosure, based on the accumulated rainfall corresponding to the runoff generated by different land use types, the runoff curve number model can be queried to determine the initial loss rainfall value, infiltration rainfall value, loss coefficient and dimensionless curve number corresponding to each land use type. ), infiltration rainfall value (S), loss coefficient (λ) and dimensionless curve number (CN) are shown in Table 4.

[0110] Table 4

[0111]

[0112] According to the embodiments of the present disclosure, the total runoff depth values ​​per unit area for different land use types under different precipitation types in 20XX can be calculated based on the accumulated precipitation and runoff parameter values, as shown in Table 5.

[0113] Table 5

[0114]

[0115] Among them, precipitation types may include light rain, moderate rain and heavy rain. The precipitation corresponding to light rain is 2-10mm, the precipitation corresponding to moderate rain is 10-25mm, and the precipitation corresponding to heavy rain is more than 25mm. The total annual runoff depth is the total value of the total runoff depth values ​​of the same precipitation type and the same land use type in 20XX, which can be obtained by adding the total runoff depth values ​​of the flood season, the total runoff depth values ​​of the normal water season, and the total runoff depth values ​​of the dry season corresponding to the same precipitation type and the same land use type in 20XX. The total precipitation runoff depth can be obtained by calculating the sum of the total runoff depth values ​​of the flood season under each precipitation type, the sum of the total runoff depth values ​​of the normal water season under each precipitation type, and the sum of the total runoff depth values ​​of the dry season under each precipitation type.

[0116] According to an embodiment of the present disclosure, for each geographic grid unit, the theoretical values ​​of multiple pollutant outputs under different precipitation types corresponding to each geographic grid unit can be calculated based on the land coverage area, average pollutant concentration value, and total runoff depth value corresponding to the land use type included therein. Figures 3 (a), 3 (b), and 3 (c) schematically illustrate schematic diagrams of multiple theoretical values ​​of pollutant outputs under different precipitation types according to an embodiment of the present disclosure. Specifically, Figures 3 (a) to 3 (c) schematically illustrate multiple theoretical values ​​of pollutant outputs under different precipitation types corresponding to a certain geographic grid unit. The pollutant may be phosphorus, and the theoretical value of total phosphorus concentration may characterize the above theoretical value of pollutant output, and the unit of the theoretical value of total phosphorus concentration is mg / L. As can be seen from Figure 3, for the same geographic grid unit, the theoretical values ​​of pollutant outputs corresponding to different precipitation types may be different.

[0117] According to an embodiment of the present disclosure, landscape types of patches of different land use types within each geographic grid unit may be identified by using landscape morphological spatial pattern analysis (MSPA).

[0118] Figure 4 The schematic diagram schematically shows the landscape types of different land use types according to the embodiment of the present disclosure. Specifically, Figure 4 A schematic diagram schematically shows the landscape types of different land use types included in a certain geographic grid unit. Figure 4 As shown, for the same geographic grid unit, different land use types may include different landscape types.

[0119] According to the embodiments of the present disclosure, weight values ​​can be assigned to landscape type indicators, soil type indicators, and geographic information indicators, and the assignment results can be connected to the attribute table of each grid through the geographic information system software. The basis for the assignment is the influence of each factor on the hydrological connectivity. The greater the barrier effect of each factor on non-point source pollutants, the higher the assignment value, as shown in Table 6. The weight values ​​corresponding to each geographic characteristic indicator are shown in Table 6. It should be noted that the categories and weight values ​​of the geographic characteristic indicators in Table 6 are only for schematic illustration, and the specific weight values ​​are not limited. Among them, the geographic information system software can be used for data management, map making, spatial analysis, etc.

[0120] Table 6

[0121]

[0122] According to the above weight values, different resistance surfaces can be established through geographic information system software, with the water body in a reservoir basin as the "source" and each geographic grid unit as the "sink", and the minimum cumulative resistance model (MCR model) can be used to calculate the pollutant transmission resistance value of each geographic grid unit. Based on the pollutant transmission resistance value of each geographic grid unit, the initial value of hydrological connectivity of each geographic grid unit can be calculated, and the initial value of hydrological connectivity can be standardized to obtain the standard value of hydrological connectivity, which ranges from 0 to 1.

[0123] Figures 5(a), 5(b), and 5(c) schematically illustrate the standard values ​​of hydrological connectivity under different precipitation types according to an embodiment of the present disclosure. As shown in Figure 5, the standard values ​​of hydrological connectivity corresponding to the same geographic grid unit under different precipitation types may be different.

[0124] According to the embodiments of the present disclosure, the actual value of the pollutant output of each geographic grid unit is calculated based on the theoretical value of the pollutant output and the standard value of the hydrological connectivity. The actual value of the pollutant output of each geographic grid unit can be divided into 5 categories by using the natural breakpoint method using the geographic information system software, among which the actual values ​​of the pollutant output of the 4th and 5th categories are relatively high. The geographic grid units corresponding to the actual values ​​of the pollutant output of the 4th and 5th categories can be used as the key source areas of non-point source pollution in small watersheds within a reservoir basin. The key source areas of non-point source pollution in small watersheds can be subsequently investigated and analyzed.

[0125] FIG6 (a), FIG6 (b), and FIG6 (c) schematically show the actual values ​​of pollutant output under different precipitation types according to an embodiment of the present disclosure. The actual value of pollutant output can be represented by the actual value of total phosphorus concentration, and the unit of the actual value of total phosphorus concentration is mg / L. As shown in FIG6, for the same geographic grid unit, the actual value of pollutant output under different precipitation types may be different. The greater the rainfall, the higher the actual value of pollutant output may be.

[0126] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present disclosure in detail. It should be understood that the above are only specific embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for determining key source areas of non-point source pollution in a small watershed, characterized in that: The method comprises: Acquire multiple sets of precipitation runoff process information corresponding to the geographic area to be investigated, wherein the multiple sets of precipitation runoff process information correspond to multiple precipitation runoff events occurring in the geographic area to be investigated, and the geographic area to be investigated includes N geographic grid units; Based on the multiple sets of precipitation runoff process information and the pre-acquired N sets of land information corresponding to the N geographic grid cells, generating N sets of theoretical values ​​of pollutant output corresponding to the N geographic grid cells, wherein the pollutants are generated by the precipitation runoff events; Based on N sets of pre-collected geographic information corresponding one-to-one to the N geographic grid cells, generating N sets of pollutant transmission resistance values ​​corresponding one-to-one to the N geographic grid cells; Based on the N pollutant output theoretical values ​​and the N pollutant transmission resistance values, generating N pollutant output actual values ​​corresponding to the N geographic grid units one by one; Based on the actual values ​​of the N pollutant outputs, at least one target geographic grid unit is selected from the N geographic grid units as a key source area of ​​non-point source pollution in a small watershed in the geographic area to be investigated.

2. The method according to claim 1, characterized in that The geographic grid cell corresponds to at least one land use type; The land information includes land use type information and land coverage areas corresponding to each of the land use types.

3. The method according to claim 2, characterized in that The theoretical values ​​of pollutant output in each group include K theoretical values ​​of pollutant output; The generating of N groups of theoretical values ​​of pollutant output corresponding to the N geographic grid units one by one based on the multiple groups of precipitation runoff process information and the pre-acquired N groups of land information corresponding to the N geographic grid units one by one includes: Based on a predetermined precipitation threshold, determining K precipitation types corresponding to the multiple sets of precipitation runoff process information; For each of the geographic grid cells, based on the K groups of precipitation runoff process information corresponding to the K types of precipitation types and the land use type information corresponding to the geographic grid cells, calculate the K groups of pollutant average concentration values ​​corresponding to the K types of precipitation types, and calculate the K groups of total runoff depth values ​​corresponding to the K types of precipitation types; For each of the geographic grid units, based on the K groups of average pollutant concentration values, the K groups of total runoff depth values, and the land cover area corresponding to each of the land use types, K theoretical values ​​of pollutant output corresponding to the K precipitation types are calculated.

4. The method according to claim 3, characterized in that The land use type information includes: runoff parameter values ​​corresponding to each land use type; The calculating of K groups of total runoff depth values ​​corresponding to the K precipitation types comprises: determining at least one runoff parameter value corresponding to the geographic grid cell based on at least one land use type corresponding to the geographic grid cell; determining at least one accumulated precipitation amount corresponding to the geographic grid cell based on at least one land use type corresponding to the geographic grid cell; For each of the precipitation types, a total runoff depth value corresponding to the precipitation type is calculated based on at least one runoff parameter value corresponding to the geographic grid unit and at least one accumulated precipitation amount.

5. The method according to claim 1, characterized in that The geographic information includes: geographic grid unit distance information, geographic characteristic indicators; The geographic grid unit distance information includes: a distance value from the geographic grid unit to a pollutant collection area.

6. The method according to claim 5, characterized in that The generating of N groups of pollutant transmission resistance values ​​corresponding to the N geographic grid units on a one-to-one basis based on the pre-collected N groups of geographic information corresponding to the N geographic grid units comprises: For each of the geographic grid units, determining at least one geographic characteristic indicator corresponding to the geographic grid unit, and determining a weight value corresponding to the at least one geographic characteristic indicator; For each of the geographic grid cells, based on the distance value from the geographic grid cell to the pollutant collection area and the weight value corresponding to the at least one geographic characteristic indicator, a pollutant transmission resistance value corresponding to the geographic grid cell is obtained.

7. The method according to claim 6, characterized in that The geographical characteristic indicators include: landscape type indicators, soil type indicators, and geographical information indicators; The geographic information index includes at least one of the following: an altitude index, a distance index, a slope index, a humidity index, and a vegetation coverage index.

8. The method according to claim 1, characterized in that The generating, based on the N pollutant output theoretical values ​​and the N pollutant transmission resistance values, N actual pollutant output values ​​corresponding to the N geographic grid units one by one includes: Based on the N pollutant transport resistance values, generating N hydrological connectivity standard values ​​corresponding to the N geographic grid cells one by one; Based on the N theoretical values ​​of pollutant output and the N standard values ​​of hydrological connectivity, the actual value of pollutant output is calculated.

9. The method according to claim 8, characterized in that The calculating the actual value of the pollutant output based on the N theoretical values ​​of the pollutant output and the N standard values ​​of the hydrological connectivity comprises: For each of the geographic grid cells, the product of the theoretical value of the pollutant output corresponding to the geographic grid cell and the standard value of the hydrological connectivity is calculated to obtain the actual value of the pollutant output.

10. The method according to claim 1, characterized in that The step of selecting at least one target geographic grid unit from the N geographic grid units based on the N actual values ​​of pollutant outputs comprises: Performing a predetermined classification process on the N actual values ​​of the pollutant outputs to obtain a classification process result; Based on the hierarchical processing result, at least one target geographic grid unit is screened out from the N geographic grid units.

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