Urban waterlogging simulation method based on SWMM and SIMWE coupling model

Abstract

The invention discloses an urban inland inundation simulation method based on an SWMM and SIMWE coupling model. The method comprises the steps that basic data and historical heavy rainfall data of a research area are collected and processed; rainstorm events in different recurrence periods are designed; an SWMM-SIMWE coupling model is constructed and verified; and simulating the urban inland inundation condition in a certain period of time in the future based on the designed rainstorm. According to the method, the SWMM model and the SIMWE model are combined and used for simulating an underground drainage system and surface runoff respectively, and water flow movement and accumulated water distribution under the complex urban terrain can be reflected more accurately and efficiently. According to the method, through a reasonable coupling mechanism, the problem that in the prior art, the urban drainage system and the surface runoff dynamic state are difficult to accurately and efficiently simulate is solved, and the water exchange efficiency and precision between the underground drainage system and the surface water flow are remarkably improved. The simulation result provides a scientific basis for urban inland inundation risk assessment and flood control and drainage system optimization, and is helpful for improving the disaster defense capability of cities.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban waterlogging simulation, and particularly relates to a method for simulating urban waterlogging based on a coupled model of SWMM and SIMWE. Background Art

[0002] In recent years, global climate change has led to an increase in the frequency of extreme rainfall. At the same time, with the rapid development of the urbanization process, the urban surface has become hardened and the underlying surface types have become more complex. Urban waterlogging has become a major problem seriously threatening urban infrastructure and the safety of residents' lives. High-intensity and short-duration rainfall events have led to frequent urban waterlogging and flood disasters. Therefore, accurately simulating and predicting urban waterlogging conditions is crucial for formulating effective urban disaster prevention and response strategies.

[0003] Currently, the methods for simulating urban waterlogging mainly rely on hydrodynamic models. In previous studies, one-dimensional or two-dimensional hydrodynamic models were often used for simulation. The one-dimensional hydrodynamic model is mainly used to simulate the water flow behavior in urban drainage networks. It is simple and fast to calculate and is suitable for large-scale simulation of drainage pipe systems. However, the one-dimensional model is difficult to accurately reflect the complex flow characteristics on the surface and urban terrain changes, and has limitations in simulating large-area waterlogging areas. The two-dimensional hydrodynamic model generally rasterizes the surface to simulate the flow of rainfall runoff and the distribution of waterlogging, and can better reflect the complex terrain and water flow movement on the urban surface. However, the two-dimensional model has a huge amount of calculation. Especially when the data grid resolution is high, it requires a large amount of computing resources and time. In addition, the two-dimensional model performs poorly in dealing with the underground drainage system and cannot accurately simulate the water flow dynamics of drainage pipes. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for simulating urban waterlogging based on a coupled model of SWMM and SIMWE. This method combines the one-dimensional model SWMM (Storm Water Manage Model) and the two-dimensional model SIMWE (Simulated Water Erosion), and through an appropriate coupling mechanism, improves the water volume exchange efficiency and accuracy between the underground drainage system and the surface water flow, thereby constructing a more detailed and comprehensive urban waterlogging simulation framework. The present invention can effectively simulate the interaction between surface and underground water flows, improve the simulation accuracy and efficiency, and provide a method reference for urban waterlogging prevention and control.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: A method for simulating urban waterlogging based on a coupled model of SWMM and SIMWE, the steps are as follows: Step 1: Collect and process the basic data and historical heavy rainfall data of the study area; Step 2: Design rainstorm events with different return periods; Step 3: Based on the data processed in Step 1, construct a SWMM-SIMWE coupling model, and calibrate the model parameters according to historical rainfall events and actual waterlogging conditions; Step 4: Use the SWMM-SIMWE coupling model under different return periods to simulate the urban waterlogging situation in the study area for a certain period in the future.

[0006] Furthermore, in Step 1, the basic data includes the digital elevation model (DEM) data, land use data, current drainage system data, and raster feature data derived from the DEM in the study area; among them, the drainage system data includes the layout of the drainage pipe network, pipe diameter, pipe bottom elevation, inspection well location, ground elevation of the inspection well, and bottom elevation of the inspection well, the location, drainage capacity, and inlet and outlet nodes of the rainwater pump station, sewage pump station, and outfall pump station, the location and size information of the storage tank, the characteristic curve of the storage water body, and the sewage treatment plant information; the raster feature data derived from the DEM at least includes the flow gradient vector obtained by taking the partial derivative of the terrain to determine the movement direction and speed of the surface water flow. The historical heavy rainfall data is the annual maximum daily precipitation data of the regional meteorological station. Standardize the terrain, drainage pipe network, and land use data into a format compatible with SWMM and SIMWE to ensure the consistency and integrity between different data sources. At the same time, fill in or correct the missing parts or incorrect information in the data.

[0007] Furthermore, the specific method for designing rainstorms with different return periods in Step 2 is as follows: Based on the historical rainfall data, use the annual maximum value method for rainfall sampling, perform curve fitting using the P-III distribution, and estimate the parameters in the rainstorm intensity formula by the least squares method. Adopt the Chicago design storm pattern and calculate the comprehensive rain peak position coefficient r And calculate the rainstorm intensity formula, derive the Chicago storm pattern formula, calculate the cumulative precipitation and average precipitation for each time period of the rainfall duration according to this formula, and finally obtain the design rainstorm events under different return periods and rainfall durations.

[0008] Among them, the rainstorm intensity formula is: ; In the formula: q is the design rainstorm intensity (unit: L / s / hm 2 ); t is the rainfall duration (unit: min ); P is the return period (unit: year). A 1 is the rain force parameter, that is, the 1-minute design rainfall when the return period is 1 year (unit: mm);C is the rainfall intensity variation parameter; b is the rainfall duration correction parameter, that is, a time parameter (unit: min) added to make the curve into a straight line after taking the logarithm of both sides of the rainstorm intensity formula; n is the rainstorm attenuation index.

[0009] When deriving the Chicago rain pattern formula using the rainstorm intensity formula, it is mainly divided into two parts: before and after the peak. Let the instantaneous intensity before the peak be i ( t b ), and the corresponding duration is t b . The instantaneous intensity after the peak is i ( t a ), and the corresponding duration is t a . The instantaneous rainfall intensities before and after the rain peak are: ; ; Furthermore, in step three, the specific process of constructing and calibrating the SWMM-SIMWE coupling model is as follows: Input the drainage pipe network data of the study area, set the pipe length, diameter, slope, and the geographical locations of the nodes (such as rainwater wells and inspection wells) connected to the pipes. Define the operation rules of the pumping stations, storage facilities, and sewage treatment plants to ensure that the drainage system can truthfully reflect the actual operation status during the simulation process. According to the distribution of inspection wells and the boundary range in the study area, use the Thiessen polygon method to automatically divide the sub-catchments. The inspection well nodes are used as the outlet nodes of the sub-catchments, corresponding one-to-one with the sub-catchments. Then, estimate the hydrological parameters such as the slope and permeability of the sub-catchments based on the topographic data and land cover data. Based on the inp file of the SWMM model, input the rainstorm events designed in step two to simulate the water flow dynamics in the underground drainage system, and output the out file and rpt file for subsequent analysis.

[0010] The SWMM model calculates the water flow dynamic process in the pipes by solving the one-dimensional unsteady flow equation, and then obtains the flow rate and water level height in the pipes at each moment. Its core is based on the Saint-Venant Equations, including the continuity equation and the momentum equation. The continuity equation is used to describe the conservation of flow rate. For any section of the drainage pipe, its continuity equation can be expressed as: ; In the formula, A is the cross-sectional area of the pipe ( m 2 ), Qis the flow rate in the pipeline ( m 3 / s ), t is the time ( s ), x is the spatial coordinate along the pipeline length ( m ), q is the external inflow rate per unit length of the pipeline ( m 3 / s / m ).

[0011] The momentum equation is used to describe the momentum change of the water flow. Considering factors such as the inertia, gravity, and friction of the water flow, the momentum equation can be expressed as: ; In the formula, V is the water flow velocity in the pipeline ( m / s ), g is the acceleration due to gravity ( m / s 2 ), H is the water level height ( m ), S f is the friction slope, usually calculated using the Manning formula.

[0012] Output the overflow node coordinates and the corresponding overflow rates obtained from the SWMM model simulation as txt files, import them into the ArcGIS (a geographic information system software developed by Environmental Systems Research Institute, Inc., USA) tool, and convert them into overflow vector data. ArcGIS was first released on December 27, 1999. To meet the input requirements of the SIMWE model, it is necessary to further convert the vector data into raster data, determine the raster size, and evenly distribute the overflow rate according to the rainfall duration, and calculate the overflow water volume per unit raster area (unit: mm / h), and finally the overflow raster data is output. The r.slope.aspect ("Raster - Terrain Analysis - Slope and Aspect") tool in Grass GIS (a geographic information system software developed by the U.S. Army Engineering Research Institute) is used to calculate the flow gradient vector of the terrain based on the DEM data and input it into the SIMWE model. Grass GIS began development in 1982 and was mainly used for land management and military engineering planning. By 1999, USA - CERL decided to open - source GRASS GIS, and since then it has become software that can be freely accessed, used, modified, and distributed by developers and users worldwide. Combining the overflow raster data, the flow gradient vector, and parameters such as the Manning coefficient, water flow diffusion coefficient, and threshold water depth, a SIMWE model is constructed to simulate surface water flow. The overflow results are transferred from the SWMM model to the SIMWE model to ensure that the simulation time steps of the two are consistent, and the interaction between surface and subsurface water flows is combined to simulate the waterlogging process in the study area under different rainfall conditions.

[0013] The SIMWE model regards water flow as two - dimensional shallow - water motion, and its core is the diffusion - wave equation (Diffusion - Wave Equation). This equation drives the diffusion of water flow through the flow gradient and mainly describes the continuity and motion of water flow. Its continuity equation is: ; In the formula, represents the position, t is the time ( s ), is the water depth ( m ), is the flow gradient ( m 3 / s / m 2 ), which represents the diffusion of water flow, is the input rate of external water sources ( m / s ).

[0014] The flow rate is calculated by the following formula : ; In the formula, is the flow velocity ( m / s ).

[0015] The formula for calculating the water flow velocity is: ; In the formula, is the Manning roughness coefficient, is the direction of the hydraulic gradient.

[0016] The calculation formula for the hydraulic gradient direction is as follows: ； ； In the formula, is the elevation value, is the elevation gradient.

[0017] By simulating the historical rainstorm waterlogging events in the study area, comparing and verifying the output results of the model with the waterlogging points and inundation ranges observed in history, the model parameters are further optimized to improve the model simulation accuracy.

[0018] Furthermore, the specific steps for simulating the urban waterlogging situation in the study area in Step 4 are as follows: Input different return period rainstorm events designed, including typical short-duration heavy rainfall and long-duration rainfall, into the model one by one to cover diverse rainstorm scenarios within the coverage area, ensuring that the simulation results can reflect the waterlogging risks in the area under different rainstorm characteristics. The model outputs dynamic indicators such as the overflow volume of the pipe network, the distribution of surface waterlogging, the waterlogging depth, and the distribution of waterlogging time, comprehensively characterizing the urban waterlogging characteristics from the time and space dimensions. Using the simulation results output by the model, conduct a dynamic analysis of the whole process of waterlogging formation, diffusion, and drainage, identify the key time nodes of waterlogging occurrence, the sensitive positions of pipe network overload, and the main areas where water accumulates, providing a basis for optimizing the design of the drainage system. According to the simulation results, divide the study area into low-risk areas, medium-risk areas, and high-risk areas, and combine data such as population density and economic activity distribution to identify the key protected areas with high waterlogging risks. Combine the GIS method (technology) to visually visualize the results such as the overflow volume, waterlogging depth, and water flow velocity, and generate a multi-scenario waterlogging simulation map to support the urban management department in quickly grasping the overall picture of waterlogging risks.

[0019] The present invention can achieve the following beneficial effects: By coupling the one-dimensional SWMM model with the two-dimensional SIMWE model, the present invention can more accurately simulate the interaction between the urban drainage system and surface runoff. With the optimized coupling mechanism, the water exchange efficiency and accuracy between the underground drainage system and surface water flow are effectively improved. Among them, the SIMWE model is selected for the surface runoff module, which can well adapt to complex terrain features. By using the rasterization method, the flow direction of urban surface water is described in detail, and it has more advantages in large-area waterlogging areas, can more accurately reflect the waterlogging distribution under different terrains and land uses, and effectively makes up for the limitations of the SWMM model in surface runoff simulation. In addition, the present invention realizes the separation of the calculation of the underground drainage system and the simulation of surface water flow, and through optimized data conversion and time step setting, effectively reduces the calculation cost while maintaining the accuracy. With the simulation results of the present invention, urban planners can better evaluate the urban waterlogging risk, optimize the existing drainage system design, and formulate more targeted flood control and drainage strategies, thus significantly enhancing the urban disaster prevention ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the drawings and embodiments: Figure 1 It is a roadmap of a method for simulating urban waterlogging based on the coupled model of SWMM and SIMWE of the present invention.

[0021] Figure 2 It is the overflow volume result simulated by the SWMM model under the design rainfall event with a 3-year return period and a 6-hour rainfall duration.

[0022] Figure 3 It is the accumulated water depth result simulated by the SIMWE model under the design rainfall event with a 3-year return period and a 6-hour rainfall duration.

[0023] Figure 4 It is the overflow volume result simulated by the SWMM model under the design rainfall event with a 50-year return period and a 24-hour rainfall duration.

[0024] Figure 5 It is the accumulated water depth result simulated by the SIMWE model under the design rainfall event with a 50-year return period and a 24-hour rainfall duration.

[0025] Figure 6 It is the change process of rainfall and overflow volume under the design rainfall event with a 6-hour rainfall duration for different return periods.

[0026] Figure 7 It is the correlation between the ground waterlogging depth and the ground elevation under the design rainfall event with a 6-hour rainfall duration.

[0027] Figure 8 It is the correlation between the ground waterlogging depth and the ground elevation under the design rainfall event with a 24-hour rainfall duration. Detailed implementation manners

[0028] Example 1: In this example, key areas in Yueyang urban area are selected as research examples. Rainstorm events with return periods of 3 years, 5 years, 10 years, 20 years, 30 years and 50 years for 6-hour and 24-hour durations are designed and input into the model for waterlogging simulation.

[0029] The preferred solution is as Figures 1 to 8 shown. A method for urban waterlogging simulation based on the SWMM and SIMWE coupling model, the steps are as follows: Step 1, collect and process the basic data and historical heavy rainfall data of the research area; Step 2, design rainstorm events with different return periods; Table 1 Maximum daily precipitation in Yueyang City under different return periods

[0030] Step 3, according to the data collected and processed in Step 1, construct the SWMM-SIMWE coupling model, and calibrate and verify the SWMM-SIMWE coupling model according to the historical actual rainstorm process and waterlogging points in Yueyang City on July 8, 2020. The surface flooding situation simulated by the SWMM-SIMWE coupling model can accurately reflect the actual process.

[0031] Step 4, use the SWMM-SIMWE coupling model under different return periods to simulate the urban waterlogging situation in the research area within a certain future time.

[0032] Furthermore, in Step 1, the basic data includes the digital elevation data (DEM) of the research area, land use data, current drainage system data, and grid feature data derived from the DEM; among them, the drainage system data includes the layout of the drainage pipe network, pipe diameter, pipe bottom elevation, inspection well location, inspection well ground elevation and inspection well bottom elevation, the location, drainage capacity and inlet and outlet nodes of rainwater pumping stations, sewage pumping stations and outfall pumping stations, the location and size information of storage ponds, the characteristic curve of storage water bodies, and sewage treatment plant information; the grid feature data derived from the DEM at least includes the flow gradient vector obtained by taking the partial derivative of the terrain, which is used to determine the movement direction and speed of surface water flow. The historical heavy rainfall data is the maximum daily precipitation data of regional meteorological stations year by year. Standardize data such as terrain, drainage pipe network and land use into a format compatible with SWMM and SIMWE to ensure the consistency and integrity between different data sources. At the same time, fill in or correct the missing parts or incorrect information in the data.

[0033] Furthermore, the specific methods for designing rainstorms with different return periods in Step 2 are as follows: Based on historical rainfall data, the annual maximum method is used for rainfall sampling. The P-III distribution is used for curve fitting, and the parameters in the rainstorm intensity formula are estimated by the least squares method. The Chicago design storm pattern is adopted to calculate the comprehensive rain peak position coefficient r And calculate the rainstorm intensity formula, derive the Chicago storm pattern formula, calculate the cumulative precipitation and average precipitation for each time period of the rainfall duration according to this formula, and finally obtain the design rainstorm events under different return periods and rainfall durations.

[0034] Among them, the rainstorm intensity formula is: ; In the formula: q is the design rainstorm intensity (unit: L / s / hm 2 ); t is the rainfall duration (unit: min ); P is the return period (unit: year). A 1 is the rain force parameter, that is, the 1-minute design rainfall when the return period is 1 year (unit: mm); C is the rain force variation parameter; b is the rainfall duration correction parameter, that is, a time parameter (unit: min) added to make the curve into a straight line after taking the logarithm of both sides of the rainstorm intensity formula; n is the rainstorm attenuation index.

[0035] When deriving the Chicago storm pattern formula using the rainstorm intensity formula, it is mainly divided into two parts: before and after the peak. Let the instantaneous intensity before the peak be i ( t b ), and the corresponding duration is t b . The instantaneous intensity after the peak is i ( t a ), and the corresponding duration is t a . The instantaneous rainfall intensities before and after the rain peak are: ; ; Further, in step three, the specific process of constructing and calibrating the SWMM-SIMWE coupling model is as follows: Input the drainage pipe network data of the study area, set the pipe length, diameter, slope, and the geographical locations of the nodes (such as rainwater wells and inspection wells) connected by the pipes. Define the operation rules of the pump stations, storage facilities, and sewage treatment plants to ensure that the drainage system can truthfully reflect the actual operation status during the simulation process. According to the distribution of inspection wells and the boundary range in the study area, use the Thiessen polygon method to automatically divide sub-catchments, and the inspection well nodes serve as the outlet nodes of the sub-catchments, corresponding to the sub-catchments one by one. Then, estimate the hydrological parameters such as the slope and permeability of the sub-catchments based on the topographic data and land cover data. Based on the inp file of the SWMM model, input the rainstorm events designed in step two to simulate the water flow dynamics in the underground drainage system, and output to obtain the out file and rpt file for subsequent analysis.

[0036] The SWMM model calculates the water flow dynamic process in the pipe by solving the one-dimensional unsteady flow equation, and then obtains the flow rate and water level height in the pipe at each moment. Its core is based on the Saint-Venant Equations, including the continuity equation and the momentum equation. The continuity equation is used to describe the conservation of flow rate. For any section of the drainage pipe, its continuity equation can be expressed as: ; where A is the cross-sectional area of the pipe ( m 2 ), Q is the flow rate in the pipe ( m 3 / s ), t is the time ( s ), x is the spatial coordinate along the pipe length ( m ), q is the external inflow rate per unit length of the pipe ( m 3 / s / m ).

[0037] The momentum equation is used to describe the change in the momentum of the water flow. Considering factors such as the inertia, gravity, and friction of the water flow, the momentum equation can be expressed as: ; where V is the water flow velocity in the pipe ( m / s ), g is the acceleration due to gravity ( m / s 2 ), H is the water level height ( m ), S f is the friction slope, usually calculated using the Manning formula.

[0038] Output the overflow node coordinates and the corresponding overflow discharge obtained from the SWMM model simulation as txt a file, import it into the ArcGIS tool, and convert it into overflow vector data. To meet the input requirements of the SIMWE model, it is necessary to further convert the vector data into raster data, determine the raster size, and evenly distribute the overflow discharge according to the rainfall duration, and calculate the overflow water volume per unit raster area (unit: mm / h ), and finally output the overflow raster data. Use the r.slope.aspect tool in Grass GIS to calculate the flow gradient vector of the terrain based on the DEM data and input it into the SIMWE model. Combine the overflow raster data, the flow gradient vector, and parameters such as the Manning coefficient, the water flow diffusion coefficient, and the threshold water depth to construct the SIMWE model for simulating surface water flow. Transfer the overflow result from the SWMM model to the SIMWE model, ensure that the time steps of the two simulations are consistent, and combine the interaction between surface and subsurface water flows to simulate the waterlogging process in the study area under different rainfall conditions.

[0039] The SIMWE model regards water flow as two-dimensional shallow water movement, and its core is the diffusion wave equation (Diffusion-Wave Equation). This equation drives the diffusion of water flow through the flow gradient and mainly describes the continuity and movement of water flow. Its continuity equation is: ; In the formula, represents the position, t is the time ( s ), is the water depth ( m ), is the flow gradient ( m 3 / s / m 2 ), representing the diffusion of water flow, is the input rate of external water sources ( m / s ).

[0040] Calculate the flow rate through the following formula : ; In the formula, is the flow velocity (m / s )

[0041] The formula for calculating the water flow velocity is: ; In the formula, is the Manning roughness coefficient, is the direction of the hydraulic gradient.

[0042] The formula for calculating the direction of the hydraulic gradient is: ； ； In the formula, is the elevation value, is the elevation gradient.

[0043] By simulating the historical rainstorm waterlogging events in the study area, comparing and verifying the output results of the model with the actual observed waterlogging points and inundation areas in history, further optimizing the model parameters, and improving the model simulation accuracy.

[0044] Furthermore, the specific situation of simulating the urban waterlogging in the study area in step four is as follows: Inputting different return period rainstorm events designed, including typical short-duration heavy rainfall and long-duration rainfall, into the model one by one, covering diverse rainstorm scenarios within the coverage area to ensure that the simulation results can reflect the waterlogging risks in the area under different rainstorm characteristics. The model outputs dynamic indicators such as the overflows of the pipe network, the distribution of surface waterlogging, the waterlogging depth, and the distribution of waterlogging time, comprehensively characterizing the urban waterlogging characteristics from the time and space dimensions. Using the simulation results output by the model, dynamically analyze the whole process of waterlogging formation, diffusion, and drainage, identify the key time nodes of waterlogging occurrence, the sensitive positions of pipe network overload, and the main waterlogging gathering areas, providing a basis for optimizing the design of the drainage system. According to the simulation results, divide the study area into low-risk areas, medium-risk areas, and high-risk areas, and combine data such as population density and economic activity distribution to identify the key protected areas with high waterlogging risks. Combining with the GIS method (technology), visually visualize the results such as overflows, waterlogging depths, and water flow velocities, generating multi-scenario waterlogging simulation maps to support the urban management department to quickly grasp the overall picture of waterlogging risks.

[0045] In this step, use the SWMM-SIMWE coupled model under different return periods to simulate the urban waterlogging situation in the study area in a certain future time, and use the simulation results output by the model to identify the sensitive positions of pipe network overload and the main waterlogging gathering areas. Taking the 6-hour rainfall with a 3-year return period and the 24-hour rainfall with a 50-year return period as examples, the simulated overflows are respectively as Figure 2 and Figure 4 shown, and the waterlogging depths are respectively as Figure 3 andFigure 5 As shown. Meanwhile, a dynamic analysis is carried out on the whole process of waterlogging formation, diffusion and drainage to identify the key time nodes of waterlogging occurrence. The variation process of rainfall and overflow volume in the design rainfall event with a 6-hour rainfall duration under different return periods is as Figure 6 shown. In addition, an analysis is carried out on the distribution law of urban waterlogging to study the correlation between ground elevation and waterlogging distribution and depth. Figure 7 and Figure 8 are the correlation between ground waterlogging depth and ground elevation in the design rainfall event with 6-hour and 24-hour rainfall durations respectively.

[0046] The above embodiments are only the preferred technical solutions of the present invention and should not be regarded as limitations on the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the method features in the technical solutions recorded in the claims. That is, the equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for simulating urban flooding based on the SWMM and SIMWE coupling model, characterized in that The steps include: Step 1: Collect and process basic data and historical heavy rainfall data in the study area; Step 2: Design rainstorm events with different return periods; Step 3: Based on the basic data processed in step 1 and the historical heavy rainfall data, a SWMM-SIMWE coupling model is constructed, and the parameters of the SWMM-SIMWE coupling model are calibrated according to historical rainfall events and actual waterlogging conditions; Step 4: Use the SWMM-SIMWE coupling model under different return periods to simulate the urban waterlogging conditions in the study area within a certain period of time in the future.

2. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 1 is characterized in that: In step 1, the basic data include digital elevation data, land use data, current drainage system data of the study area, and raster feature data derived from digital elevation data; Among them, the drainage system data includes: the layout of the drainage network, pipe size, pipe bottom elevation, inspection well location, inspection well ground elevation and inspection well bottom elevation, the location, drainage capacity and inlet and outlet nodes of the rainwater pumping station, sewage pumping station and external drainage pumping station, the location and size information of the regulating and storing pond, the characteristic curve of the regulating and storing water body, and the sewage treatment plant information; The grid feature data derived from the digital elevation data include: the flow gradient vector obtained by taking partial derivatives of the terrain, which is used to determine the direction and speed of surface water flow.

3. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 1 is characterized in that: In step 1, the historical heavy rainfall data are: the annual maximum daily precipitation data of regional meteorological stations. The terrain, drainage network and land use data are standardized into a format compatible with SWMM and SIMWE to ensure the consistency and integrity between different data sources. At the same time, the missing parts or erroneous information in the data are filled or corrected.

4. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 1 is characterized in that: The details of designing rainstorms with different return periods in step 2 are as follows: Based on historical rainfall data, the annual maximum value method was used to select rainfall samples, the P-III distribution was used for curve fitting, and the parameters in the rainstorm intensity formula were estimated by the least squares method. Using the Chicago design rain pattern, calculate the comprehensive rain peak position coefficient r The rainstorm intensity formula is calculated and the Chicago rain type formula is derived. The cumulative precipitation and average precipitation in each period of rainfall duration are calculated based on the formula, and finally the design rainstorm events with different recurrence periods and rainfall durations are obtained.

5. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 4 is characterized in that: The rainstorm intensity formula in step 2 is: ； Where: q To design the rainstorm intensity; t is the duration of rainfall; P is the return period; A 1 is the rainfall parameter, that is, the design rainfall per minute in a year; C is the rainfall force variation parameter; b It is the correction parameter of rainfall duration, i.e., a time parameter added to the logarithm of both sides of the rainstorm intensity formula to convert the curve into a straight line; n is the rainstorm attenuation index; When using the rainstorm intensity formula to derive the Chicago rain pattern formula, it is divided into two parts: before the peak and after the peak. Let the instantaneous intensity before the peak be i ( t b ), the corresponding duration is t b , the instantaneous intensity after the peak is i ( t a ), the corresponding duration is t a , the instantaneous rainfall intensity before and after the rain peak is: ； 。 6. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 1 is characterized in that: In step 3, the SWMM-SIMWE coupling model is constructed and calibrated and verified as follows: Input the drainage network data of the study area, set the pipe length, diameter, slope, and the geographical location of the nodes where the pipes connect; Define the operation rules of pumping stations, storage facilities and sewage treatment plants to ensure that the drainage system can accurately reflect the actual operation status during the simulation; According to the distribution and boundary range of the inspection wells in the study area, the sub-catchment area is automatically divided using the Thiessen polygon method. The inspection well nodes are used as the exit nodes of the sub-catchment area and correspond one-to-one with the sub-catchment area. Then, the hydrological parameters are estimated based on the topographic data and the land cover data, wherein the hydrological parameters include the slope and permeability of the subcatchment area; Based on SWMM model inp The file is used to input the rainstorm event designed in step 2 to simulate the water flow dynamics in the underground drainage system. out Files and rpt The file was used for subsequent analysis.

7. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 6 is characterized by: The SWMM model calculates the dynamic process of water flow in the pipe by solving the one-dimensional unsteady flow equation, and then obtains the flow rate and water level in the pipe at each moment. The SWMM model includes the Saint-Venant equation, which includes the continuity equation and momentum equation. The continuity equation is used to describe the conservation of flow. For any section of drainage pipe, the continuity equation is expressed as: ； In the formula, A is the cross-sectional area of ​​the pipe, Q is the flow rate in the pipe, t For time, x is the spatial coordinate along the length of the pipe, q is the external inflow rate per unit length of the pipe; The momentum equation is used to describe the change in momentum of water flow. Taking into account the inertia, gravity and friction of the water flow, the momentum equation is expressed as: ； In the formula, V is the water flow velocity in the pipe, g is the acceleration due to gravity, H is the water level, S f is the friction slope; The overflow node coordinates and corresponding overflow volume obtained by SWMM model simulation are output as txt The file is imported into ArcGIS tools to convert it into overflow vector data; in order to meet the input requirements of the SIMWE model, the vector data needs to be further converted into raster data, the raster size needs to be determined, the overflow volume needs to be evenly distributed according to the rainfall duration, the overflow water volume per unit grid area needs to be calculated, and finally the overflow raster data needs to be output; The r.slope.aspect tool in Grass GIS was used to calculate the flow gradient vector of the terrain based on the DEM data and input it into the SIMWE model. The SIMWE model was constructed to simulate surface water flow by combining the overflow raster data, flow gradient vector, Manning coefficient, water flow diffusion coefficient and threshold water depth. The overflow results were transferred from the SWMM model to the SIMWE model to ensure that the time steps of the two simulations were consistent. The interaction between surface and groundwater flows was combined to simulate the waterlogging process in the study area under different rainfall conditions.

8. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 6 is characterized by: The SIMWE model regards water flow as two-dimensional shallow water movement. The SIMWE model includes the diffusion wave equation, which drives the water flow diffusion through the flow gradient and is used to describe the continuity and movement of the water flow; its continuity equation is: ； In the formula, Indicates location, t For time, For water depth, is the flow gradient, which represents the diffusion of water flow, is the input rate of external water source; The flow rate is calculated by the following formula : ； In the formula, is the flow rate ( m / s ); The water flow velocity calculation formula is: ； In the formula, is the Manning roughness coefficient, is the hydraulic slope direction; The calculation formula for hydraulic slope direction is: ； ； In the formula, is the elevation value, is the elevation gradient; By simulating historical rainstorm waterlogging events in the study area, the output results of the SIMWE model are compared and verified with the actual historical observations of waterlogging points and flooding ranges, and the SIMWE model parameters are further optimized to improve the simulation accuracy of the SIMWE model.

9. The urban waterlogging simulation method based on the SWMM and SIMWE coupling model according to claim 6 is characterized by: The details of the simulated urban waterlogging situation in the study area in step 4 are as follows: The designed rainstorm events with different return periods, including typical short-duration heavy rainfall and long-duration rainfall, were input into the SWMM-SIMWE coupling model one by one to cover various rainstorm scenarios in the region, ensuring that the simulation results can reflect the waterlogging risk of the region under different rainstorm characteristics; The SWMM-SIMWE coupling model outputs dynamic indicators, including pipe network overflow, ground water distribution, water depth, and water time distribution, which comprehensively characterize the characteristics of urban waterlogging from the time and space dimensions; Using the simulation results output by the model, the entire process of waterlogging formation, diffusion, and drainage is dynamically analyzed to identify the key time nodes of waterlogging, sensitive locations of pipe network overload, and main waterlogging collection areas, providing a basis for optimizing drainage system design; Based on the simulation results, the study area was divided into low-risk, medium-risk and high-risk areas, and the key protection areas with high waterlogging risk were identified based on population density and economic activity distribution; Combined with GIS methods, the overflow volume, water accumulation depth and water flow velocity are intuitively visualized to generate multi-scenario waterlogging simulation maps, supporting urban management departments to quickly grasp the overall picture of waterlogging risks.

10. An urban waterlogging simulation system based on a SWMM and SIMWE coupling model, which adopts an urban waterlogging simulation method based on a SWMM and SIMWE coupling model according to any one of claims 1 to 9, characterized in that include: Acquisition module: used to collect and process basic data and historical heavy rainfall data in the study area; Design module: design rainstorm events with different return periods; SWMM-SIMWE coupling model building module: Based on the basic data processed in step 1 and the historical heavy rainfall data, the SWMM-SIMWE coupling model is built, and the parameters of the SWMM-SIMWE coupling model are calibrated according to the historical rainfall events and the actual waterlogging situation; Prediction output module: The SWMM-SIMWE coupling model with different return periods is used to simulate the urban waterlogging situation in the study area within a certain period of time in the future.

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