A hydraulic runoff modeling method, electronic device, and storage medium that can be reduced in dimension and simplified

Abstract

A hydraulic runoff modeling method, electronic device, and storage medium that can be reduced in dimension and simplified, belonging to the technical field of water environment engineering. To improve the efficiency of urban hydraulic runoff modeling, the present invention includes collecting rainfall time series data, raster data of the underlying surface digital elevation model (DEM), and underlying surface land use data; dividing and constructing a grid, the four vertex positions of each grid, the plane coordinate values and elevation values at the geometric center of each grid; model simplification topology construction, constructing simulated inspection well nodes, simulated pipe network connections, and simulated sub-catchment polygons based on the SWMM model, and associating each simulated sub-catchment polygon with the simulated inspection well node located at its center; calculating the runoff generation and concentration of each grid, using the precipitation time series data as the input of the SWMM model, constructing a SWMM simulated rain gauge, constructing a SWMM rain flood model, and calculating the real-time dynamic simulated water volume and water depth data in each grid. The present invention improves the simulation calculation efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of water environment engineering, and particularly relates to a hydraulic runoff modeling method, an electronic device, and a storage medium that can be dimensionally reduced and simplified. Background Art

[0002] Urban hydraulic runoff refers to the flow state of rainwater in the urban area under the action of surface runoff, infiltration, evaporation, etc., and is one of the important reasons for urban waterlogging. It is an indispensable important factor in the design and planning of urban drainage systems and waterlogging prevention. With the acceleration of the urbanization process, the problem of urban waterlogging caused by stormwater hydraulic runoff has become increasingly prominent, which not only affects the lives and property safety of citizens, but also poses a serious threat to urban infrastructure.

[0003] The rapid modeling of urban hydraulic runoff is to meet the needs of urban drainage system design and planning and waterlogging risk assessment and early warning, so as to improve the urban flood control and drainage capacity and ensure the safety of urban infrastructure and residents' lives. In the prior art, the establishment of urban hydraulic runoff models often requires a large amount of time and labor costs, and there are problems such as complex model construction, cumbersome data processing, and low calculation efficiency. Summary of the Invention

[0004] The problem to be solved by the present invention is to improve the efficiency of urban hydraulic runoff modeling, and a hydraulic runoff modeling method, an electronic device, and a storage medium that can be dimensionally reduced and simplified are proposed.

[0005] To achieve the above object, the present invention is realized through the following technical solutions:

[0006] A hydraulic runoff modeling method that can be dimensionally reduced and simplified includes the following steps:

[0007] S1. Collect data of the modeling area, including rainfall time series data, raster data of the underlying surface digital elevation model (DEM), and underlying surface land use data;

[0008] S2. Divide and construct a grid network. Based on the spatial resolution of the raster data of the DEM obtained in step S1, divide the modeling area into grid networks with the same size as the raster of the DEM, and obtain the four vertex positions of each grid, the plane coordinate values and elevation values at the geometric center of each grid;

[0009] S3. Model simplification topology construction. Based on the SWMM model, construct simulated inspection well nodes at the geometric center of each grid obtained in step S2, and connect adjacent simulated inspection well nodes with simulated pipe channels; construct simulated sub-catchment polygons with the four vertices of each grid as break points, and associate each simulated sub-catchment polygon with the simulated inspection well node located at its center;

[0010] S4. Calculate the runoff and infiltration of each grid. Use the precipitation time series data as the input of the SWMM model, construct a SWMM simulated rain gauge, and based on the underlying surface land use and DEM slope data of each grid, integrate the underlying surface land infiltration and evaporation characteristic data to optimize the subcatchment objects of the SWMM model. Combine the simulated inspection well nodes, simulated pipe connections, and simulated subcatchment polygons created in step S3 to construct a SWMM stormwater model, and calculate the real-time dynamic simulated water volume and water depth data in each grid.

[0011] Furthermore, the specific implementation method of step S1 includes the following steps:

[0012] S1.1. Collect the raster data of the digital elevation model (DEM) and the raster data of the digital orthophoto map (DOM) of the modeling area, and set the spatial resolution to 5 meters.

[0013] S1.2. Use remote sensing image interpretation software such as ArcGIS or ENVI to interpret the raster data of the digital orthophoto map (DOM) obtained in step S1.1 according to the image features to generate land use classification plot raster data.

[0014] S1.3. Collect rainfall time series data through rain gauge monitoring equipment or calculate simulated recurrence interval heavy rainfall time series data through the local storm intensity formula in the modeling area. The calculation expression is:

[0015]

[0016] where q is the design rainfall intensity, a is the rain force parameter, t is the rainfall duration, C is the rain force variation parameter, P is the recurrence interval, b is the precipitation duration correction parameter, and n is the storm attenuation index.

[0017] Furthermore, the land use classification in step S1.2 includes bare land, grassland, cultivated land, forest land, artificial surface, and water body.

[0018] Furthermore, the specific implementation method of step S2 includes the following steps:

[0019] S2.1. Traverse the grid cells in the raster data of the digital elevation model (DEM) obtained in step S1, select the grid cells that fall within the modeling area, and divide the modeling area into a grid network with the same size as the DEM grid.

[0020] S2.2. Use the four vertex plane coordinates of each grid cell obtained in step S2.1 as the vertex positions of the grid to construct grid network vector data, where the side length of each grid is d, and calculate the plane coordinate values and elevation values at the geometric centers of each grid.

[0021] Furthermore, the specific implementation method of step S3 includes the following steps:

[0022] S3.1. Use the plane coordinate values and elevation values at the geometric centers of each grid cell obtained in step S2 as the X coordinate value, Y coordinate value, and inner bottom elevation value of the SWMM simulated inspection well node object respectively. Here, X is the horizontal abscissa and Y is the horizontal ordinate. Then construct the simulated inspection well node, and set the initial maximum well depth as the difference h between the maximum elevation and the minimum elevation in the modeling area to obtain the simulated inspection well nodes in the modeling area;

[0023] S3.2. Create a simulated pipe connection object to connect adjacent simulated inspection well nodes obtained in step S3.1. For the upstream node of the simulated pipe connection object, select the one with the higher elevation among adjacent simulated inspection well nodes, and for the downstream node, select the one with the lower elevation among adjacent simulated inspection well nodes. Set the cross-sectional shape of the simulated pipe connection object as a rectangle, with the width of the simulated pipe connection object as d, the maximum height as h, and the length as the connection line length L between adjacent simulated inspection well nodes;

[0024] S3.3. Use the plane coordinate values of the four vertices of each grid cell obtained in step S2 as the vertex coordinates of the SWMM simulated subcatchment polygon, construct the simulated subcatchment polygon, and set the outlet of each simulated subcatchment to the simulated inspection well node located at its center to associate each simulated subcatchment polygon with the simulated inspection well node located at its center.

[0025] Furthermore, the specific implementation method of step S4 includes the following steps:

[0026] S4.1. Construct a SWMM simulated rain gauge and input the rainfall time series data collected in step S1 into the SWMM simulated rain gauge;

[0027] S4.2. Import the raster data of the DEM, land use classification plot raster data obtained in step S1, the grid vector data obtained in step S2, and the simulated subcatchment polygon obtained in step S3 into the ArcGIS software for overlay. Use the slope calculation tool of ArcGIS to convert the raster data of the DEM into slope raster data, and use ArcGIS to add subcatchment model parameter fields to the simulated subcatchment polygon;

[0028] S4.3. Use the tabular display zonal statistics tool of ArcGIS to analyze and statistically process the overlaid slope grid data and grid vector data in step S4.2, generate an average percentage slope zonal statistics table associated with each grid cell of the grid, and obtain the slope values in each grid cell;

[0029] S4.4. Use the tabular display and zonal statistics tool in ArcGIS to analyze the raster data of the land use classification plots and the vector data of the grid network superimposed in step S4.2, and obtain a statistical table of the land use classification areas corresponding to each grid of the grid network. Divide the land use types into permeable areas and impermeable areas. The permeable areas include bare land, grassland, cultivated land, and forest land, and the impermeable areas include artificial surfaces and water bodies. Calculate the weighted coefficient values of the areas of each land use classification in the land use classification area statistical table to obtain a statistical table of the weighted coefficients of the land use classification areas. The calculation expression is:

[0030]

[0031] where w perv is the weighted coefficient value of the area of the i-th land use classification in the permeable area type, and w imp is the weighted coefficient value of the area of the j-th land use classification in the permeable area type. S i is the area of the i-th land use classification in the permeable area type, S perv is the total area of the permeable area, S j is the area of the j-th land use classification in the impermeable area type, and S imp is the total area of the impermeable area;

[0032] S4.5. According to the statistical table of the weighted coefficients of the land use classification areas of the underlying permeable and impermeable areas calculated in step S4.4, combined with the relevant reference values of soil infiltration depression storage, Manning's N value, and Horton infiltration given in the publicly available "Storm Water Management Model User Manual SWMM" or by referring to relevant literature manuals, calculate the parameters of the underlying sub-catchments corresponding to each grid of the grid network, and batch assign them to the sub-catchment objects obtained in step S4.2.

[0033] S4.6. Export the sub-catchment data with parameter assignment completed in step S4.5, combine the rain gauges obtained in step S4.1, and the simulated inspection well nodes and simulated pipe network connections obtained in step S3 to construct a SWMM stormwater model, and then perform simulation calculations to obtain the real-time water volume and water depth data in the simulated inspection wells, which are the simulated real-time changing runoff volumes in the corresponding grids of the grid network obtained in step S2. The cumulative statistical runoff volume of all unit grids in the grid network over the entire simulation time domain is the total runoff volume of the modeling area.

[0034] An electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the above-mentioned hydraulic runoff modeling method that can be dimensionally reduced and simplified.

[0035] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the described hydraulic runoff modeling method that can be reduced in dimension and simplified.

[0036] Advantages of the present invention:

[0037] For the hydraulic runoff modeling method that can be reduced in dimension and simplified according to the present invention, by converting and simplifying the two-dimensional hydrodynamic simulation grid on the ground surface into the nodes and sub-catchment areas of a one-dimensional pipe network, and connecting the grids through the simplified pipe channels, a SWMM one-dimensional pipe network hydraulic model is constructed to quickly simulate and calculate the dynamic real-time runoff and catchment data of the two-dimensional grid on the ground surface, and obtain the real-time simulated runoff volume and the total runoff volume. The present invention effectively reduces the modeling complexity of the two-dimensional grid runoff modeling on the ground surface, improves the modeling and simulation calculation efficiency, reduces the workload, and saves costs. Description of the drawings

[0038] Figure 1 It is a flowchart of the hydraulic runoff modeling method that can be reduced in dimension and simplified according to the present invention;

[0039] Figure 2 It is a schematic diagram of the topological structure of the simplified model of the two-dimensional grid on the ground surface constructed according to the present invention. Detailed implementation manners

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be understood that the specific implementation manners described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described specific implementation manners are only a part of the implementation manners of the present invention, rather than all of the specific implementation manners. Usually, the components of the specific implementation manners of the present invention described and shown in the accompanying drawings herein can be arranged and designed in various different configurations, and the present invention can also have other implementation manners.

[0041] Therefore, the detailed description of the specific implementation manners of the present invention provided in the accompanying drawings herein is not intended to limit the scope of the present invention to be protected, but only represents the selected specific implementation manners of the present invention. All other specific implementation manners obtained by those skilled in the art based on the specific implementation manners of the present invention without creative efforts belong to the scope of protection of the present invention.

[0042] In order to further understand the content, features and effects of the present invention, the following specific implementation manners are exemplified and combined with the attached Figure 1 - Attached Figure 2 The details are as follows:

[0043] Example 1:

[0044] A hydraulic runoff modeling method that can be reduced in dimension and simplified, comprising the following steps:

[0045] S1. Collect data of the modeling area, including rainfall time series data, raster data of the underlying digital elevation model (DEM), and underlying land use data;

[0046] Further, the specific implementation method of step S1 includes the following steps:

[0047] S1.1. Collect raster data of the digital elevation model (DEM) and raster data of the digital orthophoto map (DOM) of the modeling area, and set the spatial resolution to 5 meters;

[0048] S1.2. Use remote sensing image interpretation software such as ArcGIS or ENVI to interpret the raster data of the digital orthophoto map (DOM) obtained in step S1.1 according to the image features to generate land use classification plot raster data;

[0049] Further, the land use classification in step S1.2 includes bare land, grassland, cultivated land, forest land, artificial surface, and water body;

[0050] S1.3. Collect rainfall time series data through rain gauge monitoring equipment or calculate simulated return period heavy rainfall time series data through the local storm intensity formula in the modeling area. The calculation expression is:

[0051]

[0052] Among them, q is the designed rainfall intensity, a is the rain force parameter, t is the rainfall duration, C is the rain force variation parameter, P is the return period, b is the precipitation duration correction parameter, and n is the storm attenuation index;

[0053] S2. Divide and construct a grid. Based on the spatial resolution of the raster data of the DEM obtained in step S1, divide the modeling area into grids with the same size as the DEM raster, and obtain the four vertex positions of each grid, the plane coordinate values and elevation values at the geometric center of each grid;

[0054] Further, the specific implementation method of step S2 includes the following steps:

[0055] S2.1. Traverse the grid cells in the raster data of the digital elevation model (DEM) obtained in step S1, select the grid cells that fall within the modeling area, and divide the modeling area into grids with the same size as the DEM raster;

[0056] S2.2. Using the planar coordinates of the four vertices of each grid cell obtained in step S2.1 as the vertex positions of the squares, construct grid vector data, where the side length of each square is d, and calculate the planar coordinate values and elevation values at the geometric centers of each square;

[0057] S3. Model simplification and topology construction. Based on the SWMM model, construct simulated inspection well nodes at the geometric centers of each square obtained in step S2, and connect adjacent simulated inspection well nodes with simulated conduits; construct simulated subcatchment polygons with the four vertices of each square as the break points, and associate each simulated subcatchment polygon with the simulated inspection well node located at its center;

[0058] Further, the specific implementation method of step S3 includes the following steps:

[0059] S3.1. Use the planar coordinate values and elevation values at the geometric centers of each square obtained in step S2 as the X coordinate value, Y coordinate value, and invert elevation value of the SWMM simulated inspection well node object respectively, where X is the horizontal abscissa and Y is the horizontal ordinate, and then construct simulated inspection well nodes, set the initial maximum well depth as the difference h between the maximum elevation and the minimum elevation in the modeling area, and obtain the simulated inspection well nodes in the modeling area;

[0060] S3.2. Create simulated conduit connection objects to connect adjacent simulated inspection well nodes obtained in step S3.1. For the simulated conduit connection object, select the one with the higher elevation as the upstream node and the one with the lower elevation as the downstream node among adjacent simulated inspection well nodes. Set the cross-section shape of the simulated conduit connection object as a rectangle, the width of the simulated conduit connection object as d, the maximum height as h, and the length as the connection length L between adjacent simulated inspection well nodes;

[0061] S3.3. Use the planar coordinate values of the four vertices of each square obtained in step S2 as the break point coordinates of the SWMM simulated subcatchment polygon, construct simulated subcatchment polygons, and set the outlets of each simulated subcatchment as the simulated inspection well nodes located at their centers to associate each simulated subcatchment polygon with the simulated inspection well node located at its center;

[0062] S4. Calculate the runoff and infiltration of each square. Use the precipitation time series data as the input of the SWMM model, construct a SWMM simulated rain gauge, based on the underlying surface land use and DEM slope data of each square, integrate the underlying surface land infiltration and evaporation characteristic data, optimize the SWMM model subcatchment object, and combine the simulated inspection well nodes, simulated conduit connections, and simulated subcatchment polygons created in step S3 to construct a SWMM rainstorm flood model, and calculate the real-time dynamic simulation water volume and water depth data in each square.

[0063] Further, the specific implementation method of step S4 includes the following steps:

[0064] S4.1. Construct a SWMM simulated rain gauge and input the rainfall time series data collected in step S1 into the SWMM simulated rain gauge;

[0065] S4.2. Import the raster data of the DEM, the raster data of the land use classification plots, the grid vector data obtained in step S2, and the simulated sub-catchment polygon obtained in step S3 into ArcGIS software for overlay. Use the slope calculation tool of ArcGIS to convert the raster data of the DEM into slope raster data, and use ArcGIS to add sub-catchment model parameter fields to the simulated sub-catchment polygon;

[0066] Further, the sub-catchment model parameter fields include slope Slope, rainfall Raingage, area Area, Width characteristic width, impervious percentage Imperv, permeability N value Nperv, impermeability N value Nimp, infiltration depression storage Sperv, impervious depression storage Simp, maximum infiltration rate MaxRate, minimum infiltration rate MinRate, decay coefficient Decay, soil drainage days DryTime;

[0067] S4.3. Use the table display zonal statistics tool of ArcGIS to analyze and statistically process the slope grid data and grid vector data overlaid in step S4.2, generate an average percentage slope zonal statistics table associated with each grid of the grid network, and obtain the slope values in each grid;

[0068] Further, the generated average percentage slope zonal statistics table associated with each unit grid of the grid network is slope_statistic, the associated field is the grid element ID, and the slope value slope in each unit grid is obtained;

[0069] S4.4. Use the table display zonal statistics tool of ArcGIS to analyze the land use classification plot raster data and grid vector data overlaid in step S4.2 to obtain a land use classification area statistics table associated with each grid of the grid network; divide the land use types into permeable areas and impermeable areas. The permeable areas include bare land, grassland, cultivated land, and forest land, and the impermeable areas include artificial surfaces and water bodies. Calculate the weighted coefficient values of the areas of each land use classification in the land use classification area statistics table to obtain a land use classification area weighted coefficient statistics table. The calculation expression is:

[0070]

[0071] where, w pervis the area weighted coefficient value of the i-th land use classification belonging to the permeable area type, w imp is the area weighted coefficient value of the j-th land use classification belonging to the permeable area type, S i is the area of the i-th land use classification belonging to the permeable area type, S perv is the total area of the permeable area, S j is the area of the j-th land use classification belonging to the impermeable area type, S imp is the total area of the impermeable area;

[0072] Furthermore, obtain the land use classification area statistical table area_statistic associated with each unit grid of the grid network, and the associated field is the grid element ID; calculate the area weighted coefficient values of each land use classification in the land use classification area statistical table area_statistic to obtain the land use classification area weighted coefficient statistics area_statistic_perv and area_statistic_imp;

[0073] S4.5. According to the land use classification area weighted coefficient statistical tables of the underlying permeable area and impermeable area calculated in step S4.4, combined with the publicly available "Storm Water Management Model User Manual SWMM (Version 5.1)" or the relevant reference values of soil infiltration depression storage, Manning's N value, and Horton infiltration given in the relevant literature manuals, calculate the underlying sub-catchment parameters associated with each grid of the grid network, and batch assign them to the sub-catchment objects obtained in step S4.2;

[0074] S4.6. Export the sub-catchment data with parameter assignment completed in step S4.5, combine the rain gauges obtained in step S4.1, and the simulated inspection well nodes and simulated pipe network connections obtained in step S3 to construct a SWMM rainstorm model, and then perform simulation calculations to obtain the real-time water volume and water depth data in the simulated inspection wells, which are the simulated real-time variable runoff volumes in the corresponding grids in the grid network obtained in step S2. The cumulative statistical runoff volume of all unit grids in the grid network in the entire simulation time domain is the total runoff volume of the modeling area.

[0075] Example 2:

[0076] An electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a dimension-reducible and simplified hydraulic runoff modeling method described in Example 1.

[0077] The computer device of the present invention may include devices such as a processor and a memory, for example, a single-chip microcomputer including a central processing unit. Moreover, when the processor is used to execute the computer program stored in the memory, the steps of the above-mentioned method for reducing-dimensionality and simplifying hydraulic runoff modeling are implemented.

[0078] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0079] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0080] Embodiment 3:

[0081] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the method for reducing-dimensionality and simplifying hydraulic runoff modeling described in Embodiment 1 is implemented.

[0082] The computer-readable storage medium of the present invention may be any form of storage medium readable by the processor of the computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. A computer program is stored on the computer-readable storage medium. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned method for reducing-dimensionality and simplifying hydraulic runoff modeling can be implemented.

[0083] The computer program includes computer program code, which may be in the form of source code, object code, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0084] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0085] Although the present application has been described above with reference to specific embodiments, various improvements can be made to it and components can be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application can be combined with each other in any way. The reason for not exhaustively describing the situations of these combinations in this specification is only to save space and resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A hydraulic runoff modeling method capable of dimensionality reduction and simplification, characterized in that: The steps include: S1. Collect data of the modeling area, including rainfall time series data, raster data of the underlying surface digital elevation model (DEM), and underlying surface land use data; S2. Divide and construct a grid. Based on the spatial resolution of the DEM grid data obtained in step S1, divide the modeling area into grids of the same size as the DEM grid, and obtain the four vertex positions of each grid, the plane coordinate value and the elevation value at the geometric center of each grid; S3. Simplify the topology of the model. Based on the SWMM model, a simulated manhole node is constructed at the geometric center of each square obtained in step S2. Each adjacent simulated manhole node is connected by a simulated pipe. A simulated subcatchment polygon is constructed with the four vertices of each square as inflection points. Each simulated subcatchment polygon is associated with the simulated manhole node at its center. S4. Calculate the runoff of each grid, use the precipitation time series data as the input of the SWMM model, build a SWMM simulated rain gauge, based on the underlying land use and DEM slope data of each grid, integrate the underlying land infiltration and evaporation characteristics data, optimize the SWMM model sub-catchment area object, combine the simulated inspection well nodes, simulated pipe connections and simulated sub-catchment area polygons created in step S3, build a SWMM stormwater model, and calculate the real-time dynamic simulated water volume and water depth data in each grid.

2. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 1, characterized in that: The specific implementation method of step S1 includes the following steps: S1.

1. Collect raster data of the digital elevation model (DEM) and digital orthophoto (DOM) of the modeling area, and set the spatial resolution to 5 meters; S1.

2. Using ArcGIS or ENVI remote sensing image interpretation software, interpret the raster data of the digital orthophoto map DOM obtained in step S1.1 according to the image features to generate raster data of land use classification plots; S1.

3. Collect rainfall time series data through rain gauge monitoring equipment or calculate the simulated return period heavy rainfall time series data through the local rainstorm intensity formula in the modeling area. The calculation expression is: Among them, q is the design rainfall intensity, a is the rain force parameter, t is the rainfall duration, C is the rainfall force variation parameter, P is the recurrence period, b is the precipitation duration correction parameter, and n is the rainstorm attenuation index.

3. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 2, characterized in that: The land use classification in step S1.2 includes bare land, grassland, cultivated land, forest land, artificial surface and water body.

4. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 3, characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Traverse the grid cells in the grid data of the digital elevation model DEM obtained in step S1, select the grid cells falling into the modeling area, and divide the modeling area into square grids with a size equal to the grid size of the DEM; S2.

2. Based on the plane coordinates of the four vertices of each grid cell obtained in step S2.1 as the vertex positions of the grid, construct grid vector data, where the side length of each grid is d, and calculate the plane coordinate value and elevation value at the geometric center of each grid.

5. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 4, characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. Use the plane coordinate values ​​and elevation values ​​at the geometric center of each grid obtained in step S2 as the X coordinate value, Y coordinate value, and inner bottom elevation value of the SWMM simulated inspection well node object, where X is the horizontal abscissa and Y is the horizontal ordinate. Then construct a simulated inspection well node, set the initial maximum well depth to the difference h between the maximum elevation and the minimum elevation of the modeling area, and obtain the simulated inspection well node of the modeling area; S3.

2. Create a simulated pipe connection object to connect the adjacent simulated manhole nodes obtained in step S3.

1. The upstream node of the simulated pipe connection object is selected from the adjacent simulated manhole nodes with a larger elevation, and the downstream node is selected from the adjacent simulated manhole nodes with a smaller elevation. Set the cross-sectional shape of the simulated pipe connection object to a rectangle, and the width of the simulated pipe connection object is d, the maximum height is h, and the length is the length of the line between adjacent simulated manhole nodes L; S3.

3. Use the plane coordinate values ​​of the four vertices of each grid obtained in step S2 as the inflection point coordinates of the SWMM simulated sub-catchment polygon to construct the simulated sub-catchment polygon, and set the outlet of each simulated sub-catchment to the simulated inspection well node located at its center to associate each simulated sub-catchment polygon with the simulated inspection well node located at its center.

6. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 5, characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Construct a SWMM simulated rain gauge and input the rainfall time series data collected in step S1 into the SWMM simulated rain gauge; S4.

2. Import the DEM raster data obtained in step S1, the land use classification plot raster data, the grid vector data obtained in step S2, and the simulated sub-catchment polygon obtained in step S3 into ArcGIS software for superposition, convert the DEM raster data into slope raster data using the slope calculation tool of ArcGIS, and add the sub-catchment model parameter field to the simulated sub-catchment polygon using ArcGIS; S4.

3. Using the table display zoning statistics tool of ArcGIS, analyze the slope grid data and grid vector data superimposed in step S4.2, generate a zoning statistics table of average percentage slopes corresponding to each grid of the grid, and obtain the slope value in each grid; S4.

4. Using ArcGIS's table display zoning statistics tool, analyze the land use classification plot raster data and grid vector data superimposed in step S4.2 to obtain a statistical table of land use classification areas corresponding to each grid grid; divide the land use type into permeable areas and impermeable areas, the permeable areas include bare land, grassland, cultivated land, and forest land, and the impermeable areas include artificial surfaces and water bodies, calculate the weighted coefficient value of each land use classification area in the land use classification area statistical table, and obtain a statistical table of land use classification area weighted coefficients, the calculation expression is: Among them, w perv is the weighted coefficient value of the i-th land use classification area in the infiltration area type, w imp is the weighted coefficient value of the j-th land use classification area in the infiltration area type, S i is the land use classification area of ​​the i-th type in the infiltration area type, S perv is the total area of ​​the infiltration area, S j is the land use classification area of ​​the jth type in the impervious area type, S imp is the total area of ​​impervious areas; S4.

5. According to the statistical table of land use classification area weighted coefficients of the underlying surface permeable area and impervious area calculated in step S4.4, combined with the reference values ​​of soil permeable depression water storage, Manning's N value, and Horton infiltration related to different land uses given in the public "Stormwater Management Model User Manual SWMM" or reference to relevant literature manuals, calculate the underlying surface sub-catchment parameters corresponding to each grid of the grid, and batch assign them to the sub-catchment objects obtained in step S4.2; S4.

6. Export the sub-catchment data with parameter assignment completed in step S4.5, combine it with the rain gauge obtained in step S4.1, and the simulated manhole nodes and simulated pipe connections obtained in step S3, build a SWMM stormwater model, and then perform simulation calculations to obtain the real-time water volume and water depth data in the simulated manholes, which is the simulated real-time changing runoff in the corresponding grid in the grid obtained in step S2. The statistical runoff of all unit grids in the grid in the entire simulation time domain is the total runoff of the modeling area.

7. An electronic device, characterized in that: It comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of a hydraulic runoff modeling method capable of dimensionality reduction and simplification as described in any one of claims 1 to 6 when executing the computer program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for hydraulic runoff modeling capable of dimensionality reduction and simplification as described in any one of claims 1 to 6 is implemented.

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