An urban waterlogging simulation method based on SWMM and SIMWE coupling model

Abstract

The application discloses a kind of urban waterlogging simulation methods based on SWMM and SIMWE coupling model, steps are as follows: collecting and processing the basic data and historical heavy rainfall data of research area;Design different recurrence period rainstorm event;SWMM-SIMWE coupling model is built and verified;Based on design rainstorm simulates urban waterlogging condition in a certain period in the future.Method combines SWMM and SIMWE model, respectively for simulating underground drainage system and surface runoff, can more accurately, efficiently reflect the water flow movement and water distribution under complex urban topography.The method solves the problem that it is difficult to accurately and efficiently simulate the dynamic of urban drainage system and surface runoff in the prior art through reasonable coupling mechanism, significantly improves the water exchange efficiency and accuracy between underground drainage system and surface water flow.Simulation results provide a scientific basis for urban waterlogging risk assessment and flood drainage system optimization, help to improve the disaster prevention ability of city.

Description

Technical Field

[0001] This invention belongs to the field of urban flooding simulation technology, and specifically relates to an urban flooding simulation method based on a coupled SWMM and SIMWE model. Background Technology

[0002] In recent years, global climate change has led to an increase in the frequency of extreme rainfall events. Simultaneously, rapid urbanization has resulted in harder urban surfaces and more complex underlying surface types, making urban flooding a major problem that seriously threatens urban infrastructure and the safety of residents. High-intensity, short-duration rainfall events cause frequent urban waterlogging and flooding disasters; therefore, accurate simulation and prediction of urban flooding conditions are crucial for developing effective urban disaster prevention and response strategies.

[0003] Currently, urban flooding simulation methods are mainly based on hydrodynamic models. Previous studies have often used one-dimensional or two-dimensional hydrodynamic models. One-dimensional hydrodynamic models are primarily used to simulate the flow behavior of urban drainage networks; they are simple and fast to calculate, suitable for large-scale simulations of drainage systems. However, one-dimensional models struggle to accurately reflect the complex flow characteristics of the land surface and changes in urban topography, limiting their ability to simulate large areas of water accumulation. Two-dimensional hydrodynamic models typically simulate rainfall runoff and water distribution by rasterizing the land surface, better reflecting the complex topography and water movement of urban surfaces. However, two-dimensional models are computationally intensive, especially when the data raster resolution is high, requiring significant computational resources and time. Furthermore, two-dimensional models are inadequate in handling underground drainage systems and cannot accurately simulate the dynamic flow of drainage pipes. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide an urban flooding simulation method based on a coupled SWMM and SIMWE model. 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 efficiency and accuracy of water exchange between the underground drainage system and surface water flow, thereby constructing a more detailed and comprehensive urban flooding simulation framework. This invention can effectively simulate the interaction between surface and groundwater flow, improve simulation accuracy and efficiency, and provide a methodological reference for urban flooding prevention and control.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for simulating urban flooding based on a coupled SWMM and SIMWE model, comprising the following steps:

[0007] Step 1: Collect and process basic data and historical heavy rainfall data for the study area;

[0008] Step 2: Design rainstorm events with different return periods;

[0009] Step 3: Based on the data processed in Step 1, construct the SWMM-SIMWE coupled model and calibrate the model parameters according to historical rainfall events and actual urban flooding conditions;

[0010] Step four involves coupling the SWMM-SIMWE models under different return periods to simulate urban flooding conditions in the study area over a certain period of time in the future.

[0011] Further, in step one, the basic data includes digital elevation data (DEM) of the study area, land use data, existing drainage system data, and raster feature data derived from the DEM. The drainage system data includes the layout of the drainage network, pipe dimensions, pipe bottom elevation, manhole locations, manhole surface elevation, and manhole bottom elevation; the locations, drainage capacities, and inlet / outlet nodes of stormwater pumping stations, sewage pumping stations, and external discharge pumping stations; the location and size information of storage tanks; characteristic curves of the storage water bodies; and information on sewage treatment plants. The raster feature data derived from the DEM includes at least the flow gradient vector obtained by taking the partial derivative with respect to the terrain, used to determine the direction and velocity of surface water flow. Historical heavy rainfall data consists of annual maximum daily precipitation data from regional meteorological stations. The topographic, drainage network, and land use data are standardized into a format compatible with SWMM and SIMWE to ensure consistency and integrity between different data sources. Simultaneously, missing or erroneous information in the data is filled in or corrected.

[0012] Furthermore, the specific details of designing rainstorms with different return periods in step two are as follows: Based on historical rainfall data, rainfall sampling is performed using the annual maximum value method, curve fitting is performed using the P-III type distribution, and the parameters in the rainstorm intensity formula are estimated using the least squares method. The Chicago design rainfall pattern is used to calculate the comprehensive rainfall peak location coefficient. r The formula for calculating the intensity of the rainstorm was used to derive the Chicago rainfall pattern formula. Based on this formula, the cumulative precipitation and average precipitation for each period of the rainfall duration were calculated, and finally, the design rainstorm events under different return periods and rainfall durations were obtained.

[0013] The formula for rainfall intensity is:

[0014] ;

[0015] In the formula: q To design the intensity of rainstorms (unit: L / s / hm 2 ); t Rainfall duration (unit: min ); P The return period is measured in years. A 1 represents the rainfall parameter, which is the design rainfall per minute at the time of a 1-year renewal period (unit: mm). C These are parameters related to rainfall intensity variation. b The rainfall duration correction parameter is a time parameter (unit: min) that makes the curve straight after taking the logarithm of both sides of the rainstorm intensity formula. n This is the rainstorm attenuation index.

[0016] When deriving the Chicago rainfall pattern formula using the heavy rainfall intensity formula, it is mainly divided into two parts: the pre-peak and post-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 ), corresponding to a duration of t a The instantaneous rainfall intensity before and after the rain peak is:

[0017] ;

[0018] ;

[0019] Further, in step three, the specific details of constructing and calibrating the SWMM-SIMWE coupled model are as follows: Input the drainage network data of the study area, set the pipe length, diameter, slope, and the geographical location of the pipe connection nodes (such as storm drains and manholes). Define the operating rules of pumping stations, storage facilities, and sewage treatment plants to ensure that the drainage system can accurately reflect the actual operating conditions during the simulation. Based on the distribution and boundary range of manholes in the study area, the Thiessen polygon method is used to automatically divide sub-catchments, with manhole nodes serving as outlet nodes of the sub-catchments, corresponding one-to-one with each sub-catchment. Then, hydrological parameters such as slope and permeability of the sub-catchments are estimated based on topographic data and land cover data. Based on the SWMM model... inp The file takes the rainstorm event designed in step two as input to simulate the water flow dynamics in the underground drainage system, and outputs the results. out Documents and rpt The file will be used for subsequent analysis.

[0020] The SWMM model calculates the dynamic process of water flow within a pipe by solving one-dimensional unsteady flow equations, thereby obtaining the flow rate and water level at each moment. Its core is based on the Saint-Venant Equations, including the continuity equation and the momentum equation. The continuity equation describes the conservation of flow rate; for any segment of a drainage pipe, its continuity equation can be expressed as:

[0021] ;

[0022] In the formula, A The cross-sectional area of ​​the pipe ( m 2 ), Q The flow rate in the pipe ( m 3 / s ), t For time ( s ), x Spatial coordinates along the length of the pipe ( m ), q External inflow rate per unit length of pipe ( m 3 / s / m ).

[0023] The momentum equation is used to describe the momentum change of water flow. Taking into account factors such as the inertia, gravity, and friction of the water flow, the momentum equation can be expressed as:

[0024] ;

[0025] In the formula, V The velocity of water flow in the pipe ( m / s ), g The acceleration due to gravity ( m / s 2 ), H Water level height ( m ), S f The friction slope is usually calculated using the Manning formula.

[0026] The overflow node coordinates and corresponding overflow amounts obtained from the SWMM model simulation are output as follows: txtThe file was imported into ArcGIS (Geographic Information System software developed by the Environmental Systems Research Institute, Inc.) and converted into overflow vector data. ArcGIS was first released on December 27, 1999. 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, and the overflow volume needs to be evenly distributed according to the rainfall duration to calculate the overflow volume per unit raster area (unit: mm / h The final output is overflow raster data. Using the `r.slope.aspect` tool ("raster-topography analysis-slope and orientation") in Grass GIS (Geographic Information System software developed by the U.S. Army Engineering Laboratory), the topographic flow gradient vector is calculated based on the DEM data and input into the SIMWE model. Grass GIS was developed starting in 1982, primarily for land management and military engineering planning. In 1999, USA-CERL decided to open-source Grass GIS, making it freely available, usable, modified, and distributed by developers and users worldwide. Combining overflow raster data, flow gradient vectors, and parameters such as Manning coefficient, water diffusion coefficient, and threshold 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 consistent time steps. Incorporating the interaction between surface and groundwater flow, the study simulates the urban flooding process under different rainfall conditions in the research area.

[0027] The SIMWE model treats water flow as two-dimensional shallow water motion, with the diffusion-wave equation at its core. This equation, driven by the flow gradient, primarily describes the continuity and motion of the flow. Its continuity equation is:

[0028] ;

[0029] In the formula, Indicates location, t For time ( s ), For water depth ( m ), For flow gradient ( m 3 / s / m 2 ), indicating the spread of water flow, The input rate of external water source ( m / s ).

[0030] Calculate the flow rate using the following formula. :

[0031] ;

[0032] In the formula, For flow rate ( m / s ).

[0033] The formula for calculating water flow velocity is:

[0034] ;

[0035] In the formula, The roughness coefficient is Manning's coefficient. This indicates the direction of the hydraulic gradient.

[0036] The formula for calculating the direction of hydraulic gradient is:

[0037] ；

[0038] ；

[0039] In the formula, This is the elevation value. This represents the elevation gradient.

[0040] By simulating historical rainstorm and flooding events in the study area, the model's output results are compared and verified with historically observed water accumulation points and inundation areas. This allows for further optimization of model parameters and improvement of model simulation accuracy.

[0041] Further, the specific steps in step four for simulating urban flooding in the study area are as follows: Different recurrence interval rainstorm events, including typical short-duration heavy rainfall and long-duration rainfall, are input into the model one by one to cover diverse rainstorm scenarios within the area, ensuring that the simulation results can reflect the flooding risk of the area under different rainstorm characteristics. The model outputs dynamic indicators such as pipe network overflow, surface water distribution, water depth, and water accumulation time distribution, comprehensively characterizing urban flooding characteristics from both temporal and spatial dimensions. Using the simulation results output by the model, a dynamic analysis is conducted on the entire process of water accumulation formation, diffusion, and drainage, identifying key time nodes for flooding occurrence, sensitive locations of pipe network overload, and major water accumulation areas, providing a basis for optimizing drainage system design. Based on the simulation results, the study area is divided into low-risk, medium-risk, and high-risk zones, and combined with data such as population density and economic activity distribution, key protection areas for high-risk flooding are identified. Using GIS methods (technology), results such as overflow, water depth, and water flow velocity are visualized intuitively, generating multi-scenario flooding simulation maps to support urban management departments in quickly grasping the overall picture of flooding risk.

[0042] The present invention can achieve the following beneficial effects:

[0043] By coupling a one-dimensional SWMM model with a two-dimensional SIMWE model, this invention can more accurately simulate the interaction between urban drainage systems and surface runoff. The optimized coupling mechanism effectively improves the efficiency and accuracy of water exchange between underground drainage systems and surface water flow. Specifically, the surface runoff module uses the SIMWE model, which adapts well to complex terrain features and describes the direction of urban surface water flow in detail using a rasterization method. It is particularly advantageous in large-area waterlogging areas, accurately reflecting the distribution of water accumulation under different terrains and land use conditions, effectively overcoming the limitations of the SWMM model in surface runoff simulation. Furthermore, this invention separates underground drainage system calculations from surface water flow simulations, and through optimized data transformation and time step settings, effectively reduces computational costs while maintaining accuracy. Using the simulation results of this invention, urban planners can better assess urban flooding risks, optimize existing drainage system designs, and formulate more targeted flood control and drainage strategies, thereby significantly improving the city's disaster prevention capabilities. Attached Figure Description

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0045] Figure 1 This is a roadmap for an urban flooding simulation method based on a coupled SWMM and SIMWE model, as described in this invention.

[0046] Figure 2 Overflow results simulated by the SWMM model under a rainfall event with a 3-year return period and a 6-hour rainfall duration.

[0047] Figure 3 The results of simulated water depth for the SIMWE model under a rainfall event with a 3-year return period and a 6-hour rainfall duration.

[0048] Figure 4 Overflow results simulated by the SWMM model under a rainfall event with a 50-year return period and a 24-hour rainfall duration.

[0049] Figure 5 The results of simulated water depth for the SIMWE model under rainfall events with a 50-year return period and a 24-hour rainfall duration.

[0050] Figure 6 Design the variation process of rainfall and overflow for 6-hour rainfall events with different return periods.

[0051] Figure 7 The correlation between ground water depth and ground elevation was designed for a 6-hour rainfall event.

[0052] Figure 8The correlation between ground water depth and ground elevation under a rainfall event was designed for a 24-hour rainfall duration. Detailed Implementation

[0053] Example 1:

[0054] This embodiment selects a key area in Yueyang city as a research example. Rainstorm events with durations of 6 hours and 24 hours and recurring periods of 3 years, 5 years, 10 years, 20 years, 30 years and 50 years were designed and input into the model to simulate urban flooding.

[0055] Preferred solutions include Figures 1 to 8 As shown, a method for simulating urban flooding based on a coupled SWMM and SIMWE model includes the following steps:

[0056] Step 1: Collect and process basic data and historical heavy rainfall data for the study area;

[0057] Step 2: Design rainstorm events with different return periods;

[0058] Table 1. Maximum daily precipitation in Yueyang City under different return periods

[0059]

[0060] Step 3: Based on the data collected and processed in Step 1, construct the SWMM-SIMWE coupled model. Then, calibrate and verify the SWMM-SIMWE coupled model based on the historical actual rainstorm process and waterlogging points in Yueyang City on July 8, 2020. The surface inundation situation simulated by the SWMM-SIMWE coupled model can accurately reflect the actual process.

[0061] Step four involves coupling the SWMM-SIMWE models under different return periods to simulate urban flooding conditions in the study area over a certain period of time in the future.

[0062] Further, in step one, the basic data includes digital elevation data (DEM) of the study area, land use data, existing drainage system data, and raster feature data derived from the DEM. The drainage system data includes the layout of the drainage network, pipe dimensions, pipe bottom elevation, manhole locations, manhole surface elevation, and manhole bottom elevation; the locations, drainage capacities, and inlet / outlet nodes of stormwater pumping stations, sewage pumping stations, and external discharge pumping stations; the location and size information of storage tanks; characteristic curves of the storage water bodies; and information on sewage treatment plants. The raster feature data derived from the DEM includes at least the flow gradient vector obtained by taking the partial derivative with respect to the terrain, used to determine the direction and velocity of surface water flow. Historical heavy rainfall data consists of annual maximum daily precipitation data from regional meteorological stations. The topographic, drainage network, and land use data are standardized into a format compatible with SWMM and SIMWE to ensure consistency and integrity between different data sources. Simultaneously, missing or erroneous information in the data is filled in or corrected.

[0063] Furthermore, the specific details of designing rainstorms with different return periods in step two are as follows: Based on historical rainfall data, rainfall sampling is performed using the annual maximum value method, curve fitting is performed using the P-III type distribution, and the parameters in the rainstorm intensity formula are estimated using the least squares method. The Chicago design rainfall pattern is used to calculate the comprehensive rainfall peak location coefficient. r The formula for calculating the intensity of the rainstorm was used to derive the Chicago rainfall pattern formula. Based on this formula, the cumulative precipitation and average precipitation for each period of the rainfall duration were calculated, and finally, the design rainstorm events under different return periods and rainfall durations were obtained.

[0064] The formula for rainfall intensity is:

[0065] ;

[0066] In the formula: q To design the intensity of rainstorms (unit: L / s / hm 2 ); t Rainfall duration (unit: min ); P The return period is measured in years. A 1 represents the rainfall parameter, which is the design rainfall per minute at the time of a 1-year renewal period (unit: mm). C These are parameters related to rainfall intensity variation. b The rainfall duration correction parameter is a time parameter (unit: min) that makes the curve straight after taking the logarithm of both sides of the rainstorm intensity formula. n This is the rainstorm attenuation index.

[0067] When deriving the Chicago rainfall pattern formula using the heavy rainfall intensity formula, it is mainly divided into two parts: the pre-peak and post-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 ), corresponding to a duration of t a The instantaneous rainfall intensity before and after the rain peak is:

[0068] ;

[0069] ;

[0070] Further, in step three, the specific details of constructing and calibrating the SWMM-SIMWE coupled model are as follows: Input the drainage network data of the study area, set the pipe length, diameter, slope, and the geographical location of the pipe connection nodes (such as storm drains and manholes). Define the operating rules of pumping stations, storage facilities, and sewage treatment plants to ensure that the drainage system can accurately reflect the actual operating conditions during the simulation. Based on the distribution and boundary range of manholes in the study area, the Thiessen polygon method is used to automatically divide sub-catchments, with manhole nodes serving as outlet nodes of the sub-catchments, corresponding one-to-one with each sub-catchment. Then, hydrological parameters such as slope and permeability of the sub-catchments are estimated based on topographic data and land cover data. Based on the SWMM model... inp The file takes the rainstorm event designed in step two as input to simulate the water flow dynamics in the underground drainage system, and outputs the results. out Documents and rpt The file will be used for subsequent analysis.

[0071] The SWMM model calculates the dynamic process of water flow within a pipe by solving one-dimensional unsteady flow equations, thereby obtaining the flow rate and water level at each moment. Its core is based on the Saint-Venant Equations, including the continuity equation and the momentum equation. The continuity equation describes the conservation of flow rate; for any segment of a drainage pipe, its continuity equation can be expressed as:

[0072] ;

[0073] In the formula, A The cross-sectional area of ​​the pipe ( m 2 ), Q The flow rate in the pipe ( m 3 / s ), t For time ( s ), x Spatial coordinates along the length of the pipe ( m), q External inflow rate per unit length of pipe ( m 3 / s / m ).

[0074] The momentum equation is used to describe the momentum change of water flow. Taking into account factors such as the inertia, gravity, and friction of the water flow, the momentum equation can be expressed as:

[0075] ;

[0076] In the formula, V The velocity of water flow in the pipe ( m / s ), g The acceleration due to gravity ( m / s 2 ), H Water level height ( m ), S f The friction slope is usually calculated using the Manning formula.

[0077] The overflow node coordinates and corresponding overflow amounts obtained from the SWMM model simulation are output as follows: txt Import the file into ArcGIS tools and convert it into overflow vector data. 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, and the overflow volume needs to be evenly distributed according to the rainfall duration to calculate the overflow volume per unit raster area (unit: mm / h The final output is overflow raster data. Using the `r.slope.aspect` tool in Grass GIS, the topographic flow gradient vector is calculated based on the DEM data and input into the SIMWE model. Combining the overflow raster data, flow gradient vector, and parameters such as the Manning coefficient, water diffusion coefficient, and threshold depth, the SIMWE model is constructed to simulate surface water flow. The overflow results are transferred from the SWMM model to the SIMWE model to ensure consistent time steps. By combining the interaction between surface and groundwater flow, the study simulates the urban flooding process under different rainfall conditions in the research area.

[0078] The SIMWE model treats water flow as two-dimensional shallow water motion, with the diffusion-wave equation at its core. This equation, driven by the flow gradient, primarily describes the continuity and motion of the flow. Its continuity equation is:

[0079] ;

[0080] In the formula, Indicates location, t For time ( s ), For water depth ( m ), For flow gradient ( m 3 / s / m 2 ), indicating the spread of water flow, The input rate of external water source ( m / s ).

[0081] Calculate the flow rate using the following formula. :

[0082] ;

[0083] In the formula, For flow rate ( m / s ).

[0084] The formula for calculating water flow velocity is:

[0085] ;

[0086] In the formula, The roughness coefficient is Manning's coefficient. This indicates the direction of the hydraulic gradient.

[0087] The formula for calculating the direction of hydraulic gradient is:

[0088] ；

[0089] ；

[0090] In the formula, This is the elevation value. This represents the elevation gradient.

[0091] By simulating historical rainstorm and flooding events in the study area, the model's output results are compared and verified with historically observed water accumulation points and inundation areas. This allows for further optimization of model parameters and improvement of model simulation accuracy.

[0092] Further, the specific steps in step four for simulating urban flooding in the study area are as follows: Different recurrence interval rainstorm events, including typical short-duration heavy rainfall and long-duration rainfall, are input into the model one by one to cover diverse rainstorm scenarios within the area, ensuring that the simulation results can reflect the flooding risk of the area under different rainstorm characteristics. The model outputs dynamic indicators such as pipe network overflow, surface water distribution, water depth, and water accumulation time distribution, comprehensively characterizing urban flooding characteristics from both temporal and spatial dimensions. Using the simulation results output by the model, a dynamic analysis is conducted on the entire process of water accumulation formation, diffusion, and drainage, identifying key time nodes for flooding occurrence, sensitive locations of pipe network overload, and major water accumulation areas, providing a basis for optimizing drainage system design. Based on the simulation results, the study area is divided into low-risk, medium-risk, and high-risk zones, and combined with data such as population density and economic activity distribution, key protection areas for high-risk flooding are identified. Using GIS methods (technology), results such as overflow, water depth, and water flow velocity are visualized intuitively, generating multi-scenario flooding simulation maps to support urban management departments in quickly grasping the overall picture of flooding risk.

[0093] In this step, the SWMM-SIMWE coupled models under different return periods are used to simulate urban flooding in the study area over a certain period of time in the future. The simulation results are then used to identify sensitive locations of pipe network overload and the main areas where water accumulates. Taking 6-hour rainfall with a 3-year return period and 24-hour rainfall with a 50-year return period as examples, the simulated overflow rates are as follows: Figure 2 and Figure 4 As shown, the water depths are respectively as follows: Figure 3 and Figure 5 As shown in the figure. Simultaneously, a dynamic analysis of the entire process of water accumulation, diffusion, and drainage was conducted to identify key time points in the occurrence of urban flooding. The changes in rainfall and overflow during designed rainfall events with different return periods of 6 hours are shown in the figure. Figure 6 As shown in the figure. Furthermore, the distribution patterns of urban flooding were analyzed, and the correlation between ground elevation and the distribution and depth of accumulated water was studied. Figure 7 and Figure 8 The correlation between ground water depth and ground elevation under designed rainfall events with rainfall durations of 6 hours and 24 hours were investigated.

[0094] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the method features in the technical solutions described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for urban waterlogging simulation based on a SWMM and SIMWE coupling model, characterized in that Comprise the following steps: Step one, collect and process the basic data and historical heavy rainfall data of the study area; Step two, design different return period storm events; Step three, according to the processed basic data and historical heavy rainfall data in step one, build a SWMM-SIMWE coupling model, and calibrate the parameters of the SWMM-SIMWE coupling model according to historical rainfall events and actual waterlogging conditions; Step four, simulate the urban waterlogging conditions in the study area in the future for a certain period of time under different return periods of the SWMM-SIMWE coupling model; In step one, the basic data includes 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 pipe network, the size of the pipe, the pipe bottom elevation, the location of the inspection well, the ground elevation of the inspection well and the bottom elevation of the inspection well, the location, drainage capacity and import and export nodes of the rainwater pump station, sewage pump station and external discharge 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 digital elevation data includes: the flow gradient vector obtained by taking the partial derivative of the terrain, which is used to determine the direction and speed of surface water flow; In step one, the historical heavy rainfall data is: the annual maximum daily precipitation data of the regional meteorological station, which is standardized to the format compatible with SWMM and SIMWE to ensure consistency and integrity between different data sources, and at the same time, fill in or correct the missing parts or error information in the data; In step three, the method of building 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 geographic location of the nodes connected by the pipe; Define the operation rules of pump stations, storage facilities and sewage treatment plants to ensure that the drainage system can accurately reflect the actual operation status during simulation; According to the distribution and boundary range of the inspection well in the study area, the Thiessen polygon method is used to automatically divide the subcatchment, and the inspection well node is taken as the outlet node of the subcatchment, which corresponds to the subcatchment one by one; Then estimate the hydrological parameters according to the terrain data and land cover data, the hydrological parameters include subcatchment slope and permeability; Based on SWMM model inp The designed storm event in step two is inputted to simulate the water flow dynamics in the underground drainage system, and the output out file and rpt file are used for subsequent analysis; SWMM model solves one-dimensional unsteady flow equation to calculate the dynamic process of water flow in the pipe, and then gets the flow and water level height in the pipe at each time, SWMM model includes Saint-Venant equation based on Saint-Venant equation including continuity equation and momentum equation; The continuity equation is used to describe the conservation of flow, and for any section of the drainage pipe, its continuity equation is expressed as: ； wherein A is the cross-sectional area of the pipe, Q is the flow rate in the pipe, t is time, x is the spatial coordinate along the length of the pipe, q is the external inflow rate per unit length of pipe; The momentum equation is used to describe the momentum change of water flow, considering the inertia, gravity and friction of water flow, the momentum equation is expressed as: ； wherein V is the water flow velocity in the pipe, g is the acceleration of gravity, H is the water level height, S f is the friction slope; The overflow node coordinates and corresponding overflow volume simulated by the SWMM model are output as txt The file is imported into the ArcGIS tool to convert it into overflow vector data. To adapt to 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, and the overflow volume needs to be uniformly distributed according to the rainfall duration to calculate the overflow water volume in a unit raster area, and finally the overflow raster data is output. The r.slope.aspect tool in Grass GIS is used to calculate the flow gradient vector of the terrain based on DEM data, which is input into the SIMWE model; the SIMWE model is constructed by combining the overflow grid data, flow gradient vector, Manning coefficient, flow diffusion coefficient and threshold water depth to simulate surface water flow; The overflow results from the SWMM model are transferred to the SIMWE model to ensure that the time steps simulated by the two models are consistent, and the interaction between surface and groundwater flow is simulated to simulate the flooding process of the study area under different rainfall conditions.

2. The urban waterlogging simulation method based on the coupling model of SWMM and SIMWE according to claim 1, characterized in that: The specific method of designing different return period rainstorm events in step two is as follows: Based on historical rainfall data, the annual maximum method is used to select rainfall, and the P-III type distribution is used for curve fitting, and the parameters in the rain intensity formula are estimated by the least square method; The position coefficient of the comprehensive rain peak is calculated by using the Chicago design rain type r The storm intensity formula is calculated, the Chicago rain type formula is derived, the cumulative precipitation and average precipitation of each period of rainfall duration are calculated according to the formula, and finally the design storm events under different return periods and rainfall durations are obtained.

3. The urban waterlogging simulation method based on the coupling model of SWMM and SIMWE according to claim 2, characterized in that: The rain intensity formula in step two is: ； where: q i is the design storm intensity; T is the rainfall duration; P is the return period; A 1 is the rain power parameter, i.e. the 1 min design rainfall for a return period of 1 year; C is the rain power variability parameter; b is the rainfall duration correction parameter, i.e. a time parameter added to the log of both sides of the storm intensity formula to linearize the curve; n is the storm decay exponent; The Chicago rain pattern formula is derived from the storm intensity formula, which is divided into two parts: the pre-peak and post-peak. The pre-peak intensity is i ( t b ), and the corresponding duration is t b . The post-peak intensity is i ( t a ), and the corresponding duration is t a . The pre-peak and post-peak instantaneous rainfall intensity is ； 。 4. The urban waterlogging simulation method based on the coupling model of SWMM and SIMWE according to claim 1, characterized in that: The SIMWE model regards water flow as two-dimensional shallow water movement, and the SIMWE model includes a diffusion wave equation, which is driven by the flow gradient to describe the continuity and motion of water flow; its continuity equation is: ； wherein, denotes the position, t is the time, is the water depth, is the flow gradient, which indicates the diffusion of the water flow, is the input rate of the external water source; Flow is calculated by the following equation : ； In the formula, is the flow rate; The water flow velocity calculation formula is: ； wherein is the Manning roughness coefficient, is the hydraulic slope direction; The hydraulic slope direction calculation formula is: ； ； wherein is an elevation value, is an elevation gradient; By simulating the historical rainstorm waterlogging events in the study area, the output results of the SIMWE model are compared and verified with the actual observed waterlogging points and flooded areas, and the SIMWE model parameters are further optimized to improve the simulation accuracy of the SIMWE model.

5. The urban waterlogging simulation method based on the coupling model of SWMM and SIMWE according to claim 1, characterized in that: The specific simulation of the urban waterlogging condition of the study area in step four is as follows: The designed different return period rainstorm events, including typical short duration heavy rain and long duration rain, are input into the SWMM-SIMWE coupling model one by one, covering a variety of rain situations in the region, to ensure that the simulation results can reflect the waterlogging risk of the region under different rain characteristics; The SWMM-SIMWE coupling model outputs dynamic indicators, including pipe network overflow flow, ground water distribution, waterlogging depth, and waterlogging time distribution, which comprehensively characterize the urban waterlogging characteristics from time and space dimensions; Using the simulation results output by the model, the whole process of waterlogging formation, diffusion and drainage is dynamically analyzed to identify the key time nodes of waterlogging occurrence, sensitive positions of pipe network overload and main waterlogging collection areas, providing a basis for optimizing drainage system design; According to the simulation results, the study area is divided into low risk area, medium risk area and high risk area, and combined with population density and economic activity distribution, the high risk key protection area of waterlogging is identified; Combined with GIS method, the overflow flow, waterlogging depth and water flow velocity are visually displayed to generate multi-scenario waterlogging simulation map, which supports the urban management department to quickly grasp the overall picture of waterlogging risk.

6. A system for urban waterlogging simulation based on a SWMM and SIMWE coupling model, which adopts the method for urban waterlogging simulation based on a SWMM and SIMWE coupling model according to any one of claims 1-5, characterized in that including: Acquisition module: used to collect and process the basic data and historical heavy rainfall data of the study area; Design module: design rainstorm events of different return periods; The SWMM-SIMWE coupling model construction module: according to the processed basic data and historical heavy rainfall data in step one, the SWMM-SIMWE coupling model is constructed, and the parameters of the SWMM-SIMWE coupling model are calibrated according to the historical rainfall events and the actual waterlogging situation; The prediction output module: the SWMM-SIMWE coupling model under different return periods is used to simulate the urban waterlogging situation in the research area in the future for a certain period of time.

Images