Urban waterlogging simulation method, device and equipment and storage medium

Abstract

The application provides a kind of urban waterlogging simulation method, device, equipment and storage medium, it is related to urban waterlogging risk assessment technical field.The method comprises: obtaining the basic data of the region to be studied;According to the preset rainfall process and the preset two-dimensional Gaussian moving attenuation rainfall field, the instantaneous rainfall intensity of the region to be studied at any position is determined;The instantaneous rainfall intensity of the region to be studied at any position is mapped to two-dimensional surface grid, and the net rainfall of each two-dimensional surface grid is calculated;The net rainfall of each two-dimensional surface grid is used as the surface source term of two-dimensional surface water dynamic model, and the coupling calculation of two-dimensional surface water dynamic model and one-dimensional drainage pipe network model is carried out, to obtain the water dynamic calculation result at each time;Wherein, two-dimensional surface water dynamic model and one-dimensional drainage pipe network model are constructed based on the basic data of the region to be studied;Based on the water dynamic calculation result at each time, the waterlogging of the region to be studied is spatio-temporal evolution.The application can improve the accuracy of simulation.

Description

Technical Field

[0001] This invention relates to the field of urban flooding risk assessment technology, and in particular to an urban flooding simulation method, apparatus, equipment and storage medium. Background Technology

[0002] Driven by both climate change and rapid urbanization, short-duration heavy rainfall and localized severe convective rainfall are becoming increasingly frequent, leading to a significant rise in urban flooding incidents. High-density built-up areas have a high proportion of impervious surfaces, resulting in rapid surface runoff. Coupled with limited drainage space, rainfall easily causes rapid flooding in low-lying areas, underpasses, road intersections, and areas with weak drainage capacity, severely impacting traffic, urban safety, and the lives and property of residents.

[0003] Currently, urban flooding simulations typically use a design storm event as the boundary input. In engineering applications, to simplify calculations, rainfall within the study area is usually treated as spatially uniformly distributed, or the rainfall center is fixed at a certain location, considering only the change in rainfall intensity over time. This approach has advantages such as ease of calculation and readily determined parameters.

[0004] However, current simulation methods are difficult to reflect the spatial non-uniformity of rain clusters during localized severe convective rainstorms, characterized by "strong center, weak periphery, and migration over time." They are also difficult to characterize the impact of the rain center's movement path on the time of water accumulation, the spatial center of gravity of water accumulation, the location of peak water depth, and the duration of high risk. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for simulating urban flooding, in order to solve the problem that it is currently impossible to accurately simulate urban flooding processes.

[0006] In a first aspect, embodiments of the present invention provide a method for simulating urban flooding, comprising: Acquire basic data for the area to be studied, including data on the area boundary, digital elevation model, land use type, drainage network, and underlying surface of the area to be studied. Based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuation rainfall field, the instantaneous rainfall intensity at any location in the area under study is determined. The two-dimensional Gaussian moving attenuation rainfall field includes a peak intensity term and a spatial attenuation term. The peak intensity term is based on the sequence of peak intensity changes over time at the center of the rain cluster to determine the instantaneous peak intensity at each moment. The spatial attenuation term is based on a Gaussian distribution function to determine the spatial distribution characteristics of the rainfall intensity at any location in the area under study as the distance from the rainfall center increases. The instantaneous rainfall intensity at any location in the area under study is mapped to a two-dimensional surface grid, and the net rainfall of each two-dimensional surface grid is calculated. The net rainfall of each two-dimensional surface grid is used as the surface source term of the two-dimensional surface hydrodynamic model. The two-dimensional surface hydrodynamic model and the one-dimensional drainage network model are coupled and calculated to obtain the hydrodynamic calculation results at each time point. The two-dimensional surface hydrodynamic model and the one-dimensional drainage network model are constructed based on the basic data of the area under study. Based on the hydrodynamic calculation results at each time point, the spatiotemporal evolution of waterlogging in the area under study is carried out.

[0007] In one possible implementation, determining the instantaneous rainfall intensity at any location in the area under study based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuation rainfall field includes: Based on the preset rainfall process, determine the sequence of the peak intensity of the rain cluster center changing over time; Based on the set rain cluster movement velocity vector, the initial center position of the rain cluster, and the spatial scale parameters of the rain cluster, the center position of the rain cluster is updated, and based on the two-dimensional Gaussian moving attenuation rainfall field, the instantaneous rainfall intensity at any location in the area under study is determined.

[0008] In one possible implementation, before determining the instantaneous rainfall intensity at any location in the area under study, the method further includes: Based on the area of ​​each effective two-dimensional surface grid in the study area, the Gaussian spatial weight of each effective two-dimensional surface grid, and the total area of ​​all effective two-dimensional surface grids in the study area, the rainfall intensity of each effective two-dimensional surface grid is normalized.

[0009] In one possible implementation, the Gaussian spatial weights of each effective two-dimensional surface grid are determined based on the two-dimensional Gaussian moving attenuation rainfall field.

[0010] In one possible implementation, mapping the instantaneous rainfall intensity of the area under study at any location to a two-dimensional surface grid and calculating the net rainfall of each two-dimensional surface grid includes: Based on the scale of the two-dimensional surface grid corresponding to any location and the spatial scale of the rain cluster, the rainfall intensity of each two-dimensional surface grid is determined. Based on the land use type corresponding to each two-dimensional surface grid, the rainfall loss intensity of each two-dimensional surface grid is determined; The net rainfall intensity of each two-dimensional surface grid is determined based on the difference between the rainfall intensity of each two-dimensional surface grid and the rainfall loss intensity of each two-dimensional surface grid. Based on the net rainfall intensity of each two-dimensional surface grid, the net rainfall amount of each two-dimensional surface grid is calculated.

[0011] In one possible implementation, the hydrodynamic calculation results at each moment include surface water depth, flow velocity, network head, and pipe segment flow rate; The spatiotemporal evolution of waterlogging in the area under study, based on the hydrodynamic calculation results at each time point, includes: Based on the hydrodynamic calculation results at each time point, the water accumulation range, peak water depth, peak occurrence time, high-risk initiation time, high-risk duration, and receding process of the area under study are determined. Based on the water accumulation range, peak water depth, peak occurrence time, high-risk initiation time, high-risk duration, and receding process of the area under study, the spatiotemporal evolution of urban flooding in the area under study is analyzed.

[0012] Secondly, embodiments of the present invention provide an urban flooding simulation device, comprising: The acquisition module is used to acquire basic data of the area to be studied, wherein the basic data includes data on the area boundary, digital elevation model, land use type, drainage network and underlying surface of the area to be studied; The first determining module is used to determine the instantaneous rainfall intensity at any location in the area under study based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuation rainfall field. The calculation module is used to map the instantaneous rainfall intensity at any location in the area under study to a two-dimensional surface grid, and calculate the net rainfall of each two-dimensional surface grid. The second determining module is used to take the net rainfall of each two-dimensional surface grid as the surface source term of the two-dimensional surface hydrodynamic model, and perform coupled calculations of the two-dimensional surface hydrodynamic model and the one-dimensional drainage network model to obtain the hydrodynamic calculation results at each time point; wherein, the two-dimensional surface hydrodynamic model and the one-dimensional drainage network model are constructed based on the basic data of the area to be studied. The evolution module is used to perform spatiotemporal evolution of waterlogging in the area under study based on the hydrodynamic calculation results at each time point.

[0013] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0015] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0016] In this embodiment of the invention, the instantaneous rainfall intensity at any location in the study area is determined based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuated rainfall field. This expands the traditional spatially uniform or fixed-center rainfall input into a two-dimensional Gaussian moving attenuated rainfall field, which can express the spatial structural characteristics of a rainstorm with high intensity at the center, gradually decreasing intensity at the periphery, and the rainfall center migrating over time. This makes the simulation of urban flooding more closely resemble a localized severe convective rainstorm process. By mapping the instantaneous rainfall intensity at any location in the study area to a two-dimensional surface grid, the net rainfall of each two-dimensional surface grid is calculated and used as the surface source term in the two-dimensional surface hydrodynamic model. This achieves synchronous coupling calculation of the spatiotemporal distribution of rainfall with surface runoff generation, pipe network runoff, and surface pipe network exchange processes. Furthermore, it overcomes the shortcomings of traditional uniform static rainfall input in reflecting the spatiotemporal non-uniformity of moving rainfall, enabling accurate simulation of urban flooding and providing a basis for urban flooding risk identification and early warning. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the urban flooding simulation method provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the spatial distribution of a two-dimensional Gaussian moving attenuation rainfall field provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the movement path and typical movement pattern of the rain cluster center provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of the spatiotemporal evolution of a two-dimensional Gaussian moving precipitation field under a south-to-north moving pattern provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the spatiotemporal evolution of a two-dimensional Gaussian moving precipitation field under a north-to-south moving pattern provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the spatiotemporal evolution of a two-dimensional Gaussian moving precipitation field in an east-to-west moving mode, provided by an embodiment of the present invention. Figure 7 This is a schematic diagram of the spatiotemporal evolution of a two-dimensional Gaussian moving precipitation field under a west-to-east moving pattern provided in an embodiment of the present invention; Figure 8 This is the urban flooding simulation result under the static rainfall mode provided in the embodiments of the present invention; Figure 9 This is a simulation result of urban flooding under a south-to-north movement pattern provided in an embodiment of the present invention; Figure 10This is a simulation result of urban flooding under a north-to-south movement pattern provided in the embodiments of the present invention; Figure 11 This is the simulation result of urban flooding under the east-to-west movement mode provided in the embodiments of the present invention; Figure 12 This is the simulation result of urban flooding under the west-to-east movement mode provided in the embodiments of the present invention; Figure 13 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] For localized severe convective rainstorms, the center of the rain cluster within the same rainfall event may continuously move due to wind fields, weather systems, or topographic conditions. Different directions and speeds of movement alter the spatiotemporal superposition relationship between rainfall input and urban low-lying areas, poorly drained areas, and surface runoff paths, resulting in significant differences in the extent, depth, and duration of flooding. If the assumption of uniform static rainfall or fixed-center rainfall is still adopted, it is easy to underestimate, misjudge, or miss the spatial distribution of urban flooding risk under moving rainfall conditions.

[0020] To address the aforementioned technical problems, this invention provides a method, apparatus, equipment, and storage medium for simulating urban flooding, in order to accurately predict urban flooding.

[0021] See Figure 1 The document illustrates a flowchart of the urban flooding simulation method provided in this embodiment of the invention, which is described in detail below: S110. Obtain basic data for the area to be studied.

[0022] The basic data includes the regional boundaries, digital elevation model, land use types, drainage network, and underlying surface data of the area under study. The drainage network includes data on drainage network nodes, pipe segments, and outlets. Surface roughness and infiltration parameters can be determined based on land use types.

[0023] In some embodiments, to facilitate subsequent data processing, the basic data can be processed to unify the coordinate system and time base.

[0024] S120. Based on the preset rainfall process and the preset two-dimensional Gaussian moving attenuation rainfall field, determine the instantaneous rainfall intensity at any location in the area under study.

[0025] In some embodiments, the peak intensity of the rain cluster center can first be determined as a sequence of changes over time based on a preset rainfall process. Then, the center position of the rain cluster is updated based on a set rain cluster movement velocity vector, the initial center position of the rain cluster, and the spatial scale parameters of the rain cluster. Based on a two-dimensional Gaussian moving attenuation rainfall field, the instantaneous rainfall intensity at any location in the area under study is determined.

[0026] In this embodiment, a preset rainfall process can generate a rainfall intensity time series based on the rainfall intensity formula for the area under study, the selected rainfall return period, and the rainfall duration. This time series is then discretized according to the calculation time step to obtain a sequence showing the change in peak intensity at the center of the rain cluster over time. .

[0027] In this embodiment, the spatial scale parameter of the rain cluster is determined based on the radius of the rain cluster identified by the weather radar or a preset effective influence range.

[0028] In some embodiments, considering the movement characteristics of local severe convective rain clusters, the traditional spatially uniform or fixed-center rainfall input is extended to a two-dimensional Gaussian moving attenuation rainfall field, which can express the spatial structural characteristics of rainstorms with high intensity at the center, gradual attenuation of intensity at the periphery, and migration of the rainfall center over time, making urban flooding simulation closer to local severe convective rainstorm processes.

[0029] In this embodiment, the two-dimensional Gaussian moving attenuation rainfall field includes a peak intensity term and a spatial attenuation term. The peak intensity term is based on the time sequence of the peak intensity variation at the center of the rain cluster to determine the instantaneous peak intensity at each moment. The spatial attenuation term is based on the Gaussian distribution function to determine the spatial distribution characteristics of the rainfall intensity at any location in the study area as the distance from the rainfall center increases.

[0030] Specifically, the two-dimensional Gaussian moving attenuation rainfall field is as follows: ; in, coordinates Rainfall intensity at time, mm / h; The instantaneous peak intensity at the center of the rainfall, in mm / h; is the standard deviation of the Gaussian distribution, controlling the spatial coverage of the rain cluster, in meters; and This is the updated center location of the rain cluster, which is also the center of the rainfall.

[0031] In this embodiment, for a stationary rainfall scenario, the initial center of the rain cluster is set at the geometric center of the area under study. For a moving rainfall scenario, the initial center of the rain cluster is set at the starting point of a predetermined movement path, and the movement direction is determined based on the prevailing wind direction, weather radar extrapolation trajectory, or a preset comparison scenario. The movement speed is then determined by the ratio of the movement path length to the predetermined crossing time, and this movement speed is decomposed into velocity components in two coordinate directions.

[0032] In this embodiment, the position of the rain cluster center can be updated first based on the rain cluster movement velocity vector at any given calculation time t. .

[0033] Specifically, the update method for the center position of the rain cluster is as follows: ; in, The initial center of the rain cluster Location, m; For rain clusters in The component of the directional velocity, in m / s; Let s be the reference time when the rain cluster begins to move.

[0034] Then, calculate the region under study at any location. instantaneous rainfall intensity at the location .

[0035] In some embodiments, to avoid inconsistencies in total rainfall input under different scenarios when the center of the rain cluster moves to the vicinity of the boundary of the area under study due to some rain clusters being located outside the area under study, the Gaussian spatial weights of the effective grid within the area under study can be normalized, so that each moving rainfall scenario can be compared under the same design rainfall process constraint.

[0036] In this embodiment, the rainfall intensity of each effective two-dimensional surface grid can be normalized based on the area of ​​each effective two-dimensional surface grid in the area to be studied, the Gaussian spatial weight of each effective two-dimensional surface grid, and the total area of ​​all effective two-dimensional surface grids in the area to be studied.

[0037] The Gaussian spatial weights of each effective two-dimensional surface grid are determined based on the two-dimensional Gaussian moving attenuation rainfall field.

[0038] Specifically, at each calculation moment, the coordinates of the rain cluster center are updated according to the formula for the movement of the rain cluster center. The distance between any coordinate in the area under study and the center of the rain cluster in both coordinate directions is calculated, and this distance is substituted into the two-dimensional Gaussian moving attenuation rainfall field formula to obtain the instantaneous rainfall intensity at the corresponding coordinates. Before normalization, the Gaussian spatial weight at the center of the rain cluster is the largest, and the Gaussian spatial weight at other locations continuously decreases as the distance from the center of the rain cluster increases. After normalization, the rainfall intensity of each grid is distributed according to its Gaussian spatial weight ratio, while satisfying the preset regional total rainfall input constraint. To ensure that the regional total rainfall corresponding to different movement paths remains consistent, the Gaussian spatial weights of all effective grid cells can be calculated first, and then the Gaussian spatial weights can be normalized using the grid area weighted sum. The same normalization rule is used for each comparison scenario.

[0039] For example, the first Gaussian space weights of an effective table grid The exponential decay term in the above two-dimensional Gaussian moving attenuation rainfall field formula is determined as follows: ; Based on this, to ensure that different moving rainfall scenarios have consistent total regional rainfall input under the same design rainfall process constraints, the rainfall intensity of each effective grid is area-weighted normalized according to the following relationship: ; in, For the normalized first An effective table grid at time Rainfall intensity, mm / h; For a moment The average intensity of the design rainfall area, mm / h; The area of ​​all effective surface grids within the study region is expressed in m². For the first The area of ​​an effective table grid, in m²; and The first The and the first An effective table grid at time Gaussian space weights; To effectively represent the total number of grid cells, after applying the normalization relation, the sum of the area-weighted rainfall inputs for each effectively represented grid cell equals... .

[0040] S130. Map the instantaneous rainfall intensity at any location in the area to be studied to a two-dimensional surface grid, and calculate the net rainfall of each two-dimensional surface grid.

[0041] In some embodiments, the rainfall intensity of each two-dimensional surface grid can be determined first, based on the scale of the two-dimensional surface grid corresponding to any location and the spatial scale of the rain cluster. Then, the rainfall loss intensity of each two-dimensional surface grid is determined based on the land use type corresponding to each grid. Next, the net rainfall intensity of each two-dimensional surface grid is determined based on the difference between the rainfall intensity and the rainfall loss intensity of each grid. Finally, the net rainfall amount of each two-dimensional surface grid is calculated based on its net rainfall intensity.

[0042] In this embodiment, the center coordinates of each two-dimensional surface grid can be read and substituted into a two-dimensional Gaussian moving attenuation rainfall field. When the grid scale is smaller than the spatial scale of the rain cluster, the rainfall intensity at the center of the grid is used to characterize the average rainfall intensity of the grid; when the grid scale is larger, the rainfall intensity of multiple sampling points within the grid is averaged by area weighting to determine the rainfall intensity of each two-dimensional surface grid.

[0043] In this embodiment, the rainfall loss intensity of each two-dimensional surface grid can be determined by calling infiltration, interception, or evaporation loss parameters according to the land use type corresponding to each two-dimensional surface grid.

[0044] The net rainfall intensity for each two-dimensional surface grid is calculated as follows: ; in, Rainfall intensity is represented by a two-dimensional surface grid. This represents the intensity of rainfall loss.

[0045] Finally, after converting the net rainfall intensity to the length and time units used in the hydrodynamic model, it is multiplied by the grid area and time step to obtain the net rainfall amount input to the grid in the current time step.

[0046] S140. The net rainfall of each two-dimensional surface grid is used as the surface source term of the two-dimensional surface hydrodynamic model. Coupled calculation of the two-dimensional surface hydrodynamic model and the one-dimensional drainage network model is performed to obtain the hydrodynamic calculation results at each time.

[0047] Among them, the two-dimensional surface water dynamic model and the one-dimensional drainage network model are constructed based on the basic data of the area under study.

[0048] In some embodiments, when constructing a two-dimensional surface hydrodynamic model, the boundaries of the area under study, the digital elevation model (DEM), land use data, and drainage network data can be first converted to a unified plane coordinate system and elevation datum. Then, the DEM is trimmed and regularized according to a preset grid size to obtain the center coordinates, bottom elevation, and slope of each two-dimensional surface grid. Manning roughness and infiltration parameters are assigned to each grid based on land use type. Based on the drainage network data, drainage nodes, pipe segments, and outlets are set as computational nodes, connecting pipes, and outflow boundaries of the one-dimensional drainage network model, respectively. Length, cross-sectional dimensions, bottom elevation, and roughness are configured for each pipe segment. The two-dimensional surface hydrodynamic model uses the finite volume method to discretize the two-dimensional shallow water equations, while the one-dimensional SWMM drainage network model uses the dynamic wave method to solve the Saint-Venant equations for pipes and the continuity equations for nodes. For each drainage node, the corresponding or nearest two-dimensional surface grid is determined based on its plane coordinates, and a spatial index is established between the drainage node and the surface grid. Within each coupling time step, the water level of the corresponding surface grid and the head of the drainage node are read. The direction of surface inflow or node overflow is determined based on the head difference between the two. The exchanged water volume is deducted from the water supply side and added to the water receiving side to ensure the water volume of the coupling system is conserved.

[0049] In this embodiment, the two-dimensional surface hydrodynamic model is described using two-dimensional shallow water equations to calculate surface runoff and overflow. The one-dimensional drainage network model is described using one-dimensional Saint-Venant equations to calculate pipe and channel runoff and outlet outflow. Surface inflow or node overflow is calculated based on the difference in head between the surface water level and the drainage nodes, and the surface grid water volume and drainage network water volume are updated synchronously.

[0050] In this embodiment, the governing equations of the two-dimensional surface hydrodynamic model are: , ; ; in, t For calculating time, seconds; It is a variable vector, including water depth. h ,m; and respectively along x and y Unit width flow rate in the direction, m 2 / s; and respectively along x and y Flux in the direction; The acceleration due to gravity is m / s². 2 ; and respectively along x andy Flow velocity in the direction, m / s; The source terms are represented, including precipitation source terms, bottom slope source terms, and friction source terms. This refers to the elevation of the riverbed bottom. The coefficient of friction of the bed surface can be expressed as: ,in N Manning coefficient, s / m 1 / 3 .

[0051] Specifically, the net rainfall intensity of each grid is written as an area source term into the continuity equation of the two-dimensional surface hydrodynamic model. The internal computation time step of the two-dimensional model is determined based on the CFL stability condition. The finite volume method is used to calculate the water flux at the interface between adjacent grids, and the numerical flux is obtained using the HLLC approximate Riemann solver. Spatial reconstruction is performed using the TVD-MUSCL format, and the two-step Runge-Kutta method is used to advance the water depth and unit width flow rate in both coordinate directions. The one-dimensional drainage network model uses the dynamic wave method to synchronously update the flow rate of each pipe segment and the head of each node. When the calculation progresses to the coupling moment, the exchange water volume between the surface and the network is calculated based on the node-grid spatial index. This exchange water volume is then used as source and sink terms of equal size but opposite sign in the two-dimensional surface grid and the one-dimensional network nodes, respectively. After updating the exchange water volume, the next time step is initiated.

[0052] During the model simulation calculation, based on the two-dimensional surface water level elevation Surface elevation and water level elevation at pipeline nodes The relationship among these three factors divides the water exchange between the two-dimensional surface and the underground drainage network into three scenarios.

[0053] When the water level at the rainwater node Below ground elevation At this time, surface backflow occurs, meaning surface water flows into the storm drain. The weir flow formula is then used to calculate the amount of water flowing into the storm drain node: ; in, The flow rate from the surface into the rainwater node is expressed in m³ / s. This is the weir flow coefficient; Let be the equivalent weir width of the rainwater well, in meters. This is the difference between the surface water level and the ground elevation. .

[0054] When the water level at the rainwater node Z j Below the surface water level Z w But higher than the surface elevation Z gAt this time, surface water will flow back to the rainwater node through the rainwater well. The amount of water flowing into the rainwater node at this time can be calculated using the orifice flow formula: ; in, The flow rate (m) of rainwater flowing into the well from the surface. 3 / s; This is the orifice coefficient (0.8 for rounded corners and 0.6 for square corners). The area of ​​the inlet orifice of the rainwater node, in meters. 2 ; The difference between surface water and ground elevation. .

[0055] When the water level at the rainwater node Above the surface water level At this time, nodal overflow occurs, meaning water from the storm drain overflows to the surface. The overflow volume can be calculated using the orifice flow formula: ; in, Let m be the overflow flow rate at the node. 3 / s; This represents the difference between the node water level and the surface grid water level. .

[0056] In some embodiments, surface runoff, surface runoff, pipe network runoff, and inflow or overflow between the surface and the pipe network at each time point can be calculated to obtain hydrodynamic elements such as surface water depth, flow velocity, pipe network head, and pipe segment flow rate.

[0057] In this embodiment, at each time step, the water volume of each surface grid can be updated based on the area source term of net rainfall and the flux of adjacent grid interfaces to calculate the surface runoff and overflow processes. The network runoff, node head, and outlet outflow are updated based on the one-dimensional network node inflow, pipe segment continuity equation, and momentum equation. The water levels of the correlated surface grids are compared with the drainage node heads: when the node head is lower than the surface water level, the water volume flowing from the surface into the network is calculated. When the node head is higher than the surface water level, the water volume overflowing from the node to the surface is calculated, and the water volume of the corresponding grid and node is synchronously corrected according to the exchange direction. At each preset output time, the water depth and flow velocity in both coordinate directions of all surface grids, as well as the head of all drainage nodes and the flow rate of each pipe segment, are recorded.

[0058] S150. Based on the hydrodynamic calculation results at each time point, the spatiotemporal evolution of waterlogging in the study area is carried out.

[0059] In some embodiments, the water accumulation range, peak water depth, peak occurrence time, high-risk initiation time, high-risk duration, and receding process of the area under study can be determined based on the hydrodynamic calculation results at each time point. Then, based on the water accumulation range, peak water depth, peak occurrence time, high-risk initiation time, high-risk duration, and receding process of the area under study, the spatiotemporal evolution of urban flooding in the area under study is performed.

[0060] In this embodiment, the water accumulation range, peak water depth, peak occurrence time, high-risk initiation time, high-risk duration, and receding process of the target area can be identified based on the hydrodynamic calculation results at each moment.

[0061] Specifically, at each output time, surface grids with water depths reaching a preset water accumulation threshold and spatially adjacent are merged into water accumulation patches, and the sum of the grid areas within each patch represents the water accumulation range. For each grid or target water accumulation point, its full-time water depth data is traversed, the maximum water depth is determined as the peak water depth, and the corresponding peak occurrence time is recorded. The moment when the water depth first reaches a preset high-risk threshold is determined as the high-risk initiation moment, and the duration for which the water depth is continuously above this high-risk threshold is determined as the high-risk duration. Starting from the peak occurrence moment, the moment when the water depth first drops below a preset receding threshold is searched, and the water depth decay sequence within this period is output to represent the receding process.

[0062] Among them, the water accumulation threshold, high-risk threshold and water receding threshold are preset according to the risk assessment standards adopted in the target area and remain consistent in each comparison scenario.

[0063] In some embodiments, the direction of rain cluster movement, speed of movement, initial position, spatial scale parameters, or rainfall recurrence period can be changed to form multiple moving rainfall scenarios, which are then simulated separately, and the results of urban flooding risk under different scenarios are compared and analyzed.

[0064] In this embodiment, moving rainfall scenarios can be constructed using single-factor control or full-factor combination methods, while maintaining the design rainfall duration, numerical grid, surface parameters, pipeline parameters, and index thresholds unchanged across scenarios. For each combined scenario, the processes of rainfall field generation, net rainfall calculation, coupled solution, and index extraction are executed sequentially to establish the correspondence between scenario number and input parameters, spatiotemporal distribution of rainfall, and urban flooding risk indicators. Different scenarios are then compared based on indicators such as maximum water accumulation range, peak water depth, and high-risk duration.

[0065] Moving precipitation scenarios can include one or more of the following: stationary precipitation patterns, south-to-north moving patterns, north-to-south moving patterns, east-to-west moving patterns, and west-to-east moving patterns; or they can include moving paths determined by precipitation fields retrieved from meteorological radar, short-term forecast data, prevailing wind direction, or topographic slope direction.

[0066] In some embodiments, the system may also output the spatiotemporal distribution of rainfall, the trajectory of the rain cluster center, the process of water accumulation depth, the maximum water accumulation range, the time of peak water depth, and the identification results of the stage of urban flooding risk under moving rainfall conditions.

[0067] In this embodiment, the two-dimensional Gaussian precipitation field calculation module outputs the grid precipitation intensity and rain cluster center coordinates at each calculation time. The two-dimensional surface hydrodynamic model outputs the water depth and flow velocity time series for each surface grid; the one-dimensional drainage network model outputs the head of each drainage node, the flow rate of each pipe segment, and the outflow time series for each outlet. The urban flooding index identification module statistically analyzes the hydrodynamic calculation results to obtain the maximum water accumulation range, peak water depth and its occurrence time, high-risk initiation time, high-risk duration, and receding process. The above results are stored in raster files containing spatial coordinates and time identifiers, time series data tables, and index summary tables.

[0068] This invention can quantify the impact of different rain cluster movement paths, directions, speeds, and spatial scales on the time of water accumulation, the extent of water accumulation, peak water depth, peak occurrence time, and duration of high risk, providing a process-based basis for identifying urban flood-prone areas under moving rainfall conditions.

[0069] This invention determines the instantaneous rainfall intensity at any location in the study area based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuated rainfall field. It extends the traditional spatially uniform or fixed-center rainfall input to a two-dimensional Gaussian moving attenuated rainfall field, which can express the spatial structural characteristics of a rainstorm with high intensity at the center, gradually decreasing intensity at the periphery, and the rainfall center migrating over time. This makes urban flooding simulation more closely resemble localized severe convective rainstorm processes. By mapping the instantaneous rainfall intensity at any location in the study area to a two-dimensional surface grid, the net rainfall of each two-dimensional surface grid is calculated and used as the surface source term in the two-dimensional surface hydrodynamic model. This achieves synchronous coupling calculation of the spatiotemporal distribution of rainfall with surface runoff generation, pipe network runoff, and surface pipe network exchange processes. Furthermore, it overcomes the shortcomings of traditional uniform static rainfall input in reflecting the spatiotemporal non-uniformity of moving rainfall, enabling accurate simulation of urban flooding and providing a basis for urban flooding risk identification and early warning.

[0070] To verify the accuracy of the urban flooding simulation method provided by this invention, a typical urban built-up area was selected as the research object. This area is a high-density urban built-up environment, where local topographic undulations, road depressions, and areas with weak drainage have a significant impact on the rainstorm water accumulation process. In this embodiment, digital elevation models, land use data, drainage network node and segment data, outlet data, and design rainstorm parameters of the study area were obtained to construct a coupled hydrodynamic model of a two-dimensional surface hydrodynamic model and a one-dimensional drainage network model, which was used to simulate the urban flooding process under moving rainfall conditions.

[0071] This embodiment selects a typical built-up area of ​​8.35 km² in Changzhi City as the study area. The basic data includes a 3m×3m resolution digital elevation model, orthophotos, land use data, drainage network node and segment data, outlet data, and design stormwater parameters obtained through UAV aerial surveying. The digital elevation model contains approximately 928,193 regular grids, and the study area is divided into 9 land use types based on the orthophotos. All spatial data are unified to the same plane coordinate system and elevation datum, clipped according to the boundaries of the study area, and abnormal elevations and invalid grids are checked and processed.

[0072] First, the digital elevation model is subjected to coordinate unification, boundary clipping, and gridding. Surface roughness and infiltration parameters are then determined based on land use type. A one-dimensional drainage network model is established based on drainage network data, including rainwater nodes, pipe segments, and outlets. The inflow, overflow, and outlet boundary relationships between the two-dimensional surface grid and the drainage nodes are also established.

[0073] For each 3m×3m surface grid, its center coordinates, bottom elevation, and land use type are recorded, and corresponding Manning roughness and steady-state infiltration rates are assigned. The two-dimensional surface model is spatially discretized using the finite volume method, numerical fluxes are calculated using the HLLC approximate Riemann solver, spatial reconstruction is performed using the TVD-MUSCL scheme, and time progression is achieved using the two-step Runge–Kutta method, with the computation time step controlled by CFL conditions. A one-dimensional SWMM model including rainwater nodes, pipe segments, and outlets is established based on drainage network data. Length, cross-sectional dimensions, upstream and downstream bottom elevations, and roughness are set for each pipe segment, and the hydrodynamic process is solved using the dynamic wave method. The corresponding surface grid is determined based on the planar coordinates of each drainage node. Within each coupled time step, the relative relationship between surface water level, ground elevation, and node head is used to determine the state of conventional inflow, submerged inflow, or node overflow, and the calculated exchange water volume is transferred equally between the corresponding surface grid and the network nodes.

[0074] Secondly, based on short-duration rainfall data and storm intensity formulas for the study area, design rainfall processes with different return periods are generated. These design rainfall processes are used to determine the time sequence of the peak intensity at the center of the rain cluster. .

[0075] Based on the formula for the intensity of heavy rainfall in Changzhi City, three rainfall return periods of 10 years, 20 years, and 50 years were set, and the rainfall duration was set to 120 minutes. The comprehensive peak rainfall coefficient was determined to be 0.375 based on short-duration rainfall data from 1981 to 2016. The Chicago rainfall pattern was used to distribute the design rainfall for each return period to discrete time steps, ensuring that the rainfall intensity reached its peak approximately 45 minutes after the start of rainfall. The resulting time-intensity sequence was used to characterize the change in peak intensity at the center of the rain cluster over time.

[0076] The formula used for the intensity of rainstorms in Changzhi City is: ; Where q is the rainfall intensity (mm / min), P is the rainfall return period (year), and t is the rainfall duration (min). P is set to 10, 20, and 50, and combined with the comprehensive peak rainfall coefficient r=0.375, the Chicago rain pattern is used to distribute the design rainfall intensity of the corresponding return period to each discrete time step, obtaining a sequence of the peak intensity at the center of the rain cluster changing over time.

[0077] Again, such as Figure 2 As shown, the rainfall input is spatially constructed as a two-dimensional Gaussian moving attenuation rainfall field, and the spatial scale parameters of the rain clusters are set. At each calculation moment, the coordinates of the rain cluster center are updated based on the rain cluster's movement velocity vector, and the rainfall intensity at the center of each two-dimensional surface grid is calculated using the two-dimensional Gaussian moving attenuation rainfall field formula. Subsequently, infiltration, interception, or other rainfall losses are deducted to form the net rainfall source term.

[0078] The spatial scale parameter of the rain cluster was set to 750 m in both coordinate directions, corresponding to an effective influence diameter of approximately 4500 m. For the stationary rainfall pattern, the rain cluster center was fixed at the geometric center of the study area. For the four moving rainfall patterns, movement paths were set from south to north, from north to south, from east to west, and from west to east, respectively. The movement paths were determined based on the study area boundary and the direction of the core road, and the velocity components in both coordinate directions were determined based on the length of the movement path and the predetermined traversal time. Within each time step, the coordinates of the rain cluster center were first updated according to the rain cluster center movement formula, then the rainfall intensity at the center of each surface grid was calculated according to the two-dimensional Gaussian moving attenuation rainfall field formula, and finally, the corresponding rainfall loss was deducted according to the land use type to obtain the net rainfall input as the surface source term of the two-dimensional surface hydrodynamic model.

[0079] To analyze the impact of rain cluster movement direction on urban flooding processes, such as Figure 3 As shown, this embodiment sets up five rainfall movement patterns: M0 is a stationary rainfall pattern, where the rain cluster center is fixed at the geometric center of the study area; M1 is a south-to-north movement pattern; M2 is a north-to-south movement pattern; M3 is an east-to-west movement pattern; and M4 is a west-to-east movement pattern. Each pattern uses the same rainfall return period, rainfall duration, and rain cluster spatial scale parameters, only changing the movement path and velocity direction of the rain cluster center.

[0080] Fifteen hydrodynamic simulation scenarios were constructed by combining three rainfall return periods with five rain cluster movement patterns. Each scenario used the same 120-minute rainfall duration, two-dimensional surface grid, drainage network parameters, rain cluster spatial scale parameters, and result output time intervals, only varying the rainfall return period and the rain cluster center movement path. Each scenario started independently from the same initial hydrodynamic state to avoid the transfer of surface water depth or drainage network conditions between different scenarios.

[0081] Within each computation time step, the location of the rain cluster center is first updated and the rainfall intensity of the two-dimensional surface grid is calculated. Then, the net rainfall source term is input into the two-dimensional surface hydrodynamic model to calculate the surface runoff generation and overflow processes. Simultaneously, a one-dimensional drainage network model is used to calculate the pipe segment runoff and outlet outflow processes, and surface inflow or node overflow is calculated based on the difference in water head between the surface water level and the drainage nodes, achieving synchronous updates of surface and network water volume. The above calculation process is repeated until the total simulation duration is reached.

[0082] After the simulation, the rainfall intensity and rain cluster center trajectory are exported from the rainfall field module, the surface water depth and velocity grid are exported from the two-dimensional surface hydrodynamic model, and the nodal head, pipe segment flow rate, and outlet outflow sequence are exported from the one-dimensional drainage network model. Using a unified index threshold, the water accumulation range, peak water depth and its occurrence time, high-risk initiation time, high-risk duration, and receding process are extracted from the above results. Using the same index system, 15 scenarios are compared, and scenarios with larger peak water depths, wider water accumulation ranges, or longer high-risk durations are identified as relatively unfavorable moving rainfall scenarios. Based on the results obtained from the above simulation, the following can be derived: Figures 4-7 The diagram illustrates the spatiotemporal evolution of a two-dimensional Gaussian moving precipitation field under different moving directions, and... Figures 8-12 The diagram shows a comparison of urban flooding simulation results under different moving rainfall scenarios.

[0083] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0084] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0085] The urban flooding simulation device provided in this embodiment of the invention, for ease of explanation, only shows the parts related to this embodiment of the invention, which are described in detail below: Urban flooding simulation device, including: The acquisition module is used to acquire basic data of the area to be studied, including the area boundary, digital elevation model, land use type, drainage network and underlying surface data of the area to be studied. The first determining module is used to determine the instantaneous rainfall intensity at any location in the area under study based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuation rainfall field. The two-dimensional Gaussian moving attenuation rainfall field includes a peak intensity term and a spatial attenuation term. The peak intensity term is based on the sequence of peak intensity changes over time at the center of the rain cluster to determine the instantaneous peak intensity at each moment. The spatial attenuation term is based on a Gaussian distribution function to determine the spatial distribution characteristics of the rainfall intensity at any location in the area under study as the distance from the rainfall center increases. The calculation module is used to map the instantaneous rainfall intensity at any location in the area under study to a two-dimensional surface grid and calculate the net rainfall of each two-dimensional surface grid. The second determination module is used to take the net rainfall of each two-dimensional surface grid as the surface source term of the two-dimensional surface hydrodynamic model, and perform coupled calculations of the two-dimensional surface hydrodynamic model and the one-dimensional drainage network model to obtain the hydrodynamic calculation results at each time point; wherein, the two-dimensional surface hydrodynamic model and the one-dimensional drainage network model are constructed based on the basic data of the area to be studied. The evolution module is used to perform spatiotemporal evolution of waterlogging in the study area based on the hydrodynamic calculation results at each time point.

[0086] In one possible implementation, the first determining module is used to determine the sequence of the peak intensity of the rain cluster center changing over time based on a preset rainfall process; Based on the set rain cluster movement velocity vector, the initial center position of the rain cluster, and the spatial scale parameters of the rain cluster, the center position of the rain cluster is updated, and the instantaneous rainfall intensity at any location in the study area is determined based on the two-dimensional Gaussian moving attenuation rainfall field.

[0087] In one possible implementation, the first determining module is used to normalize the rainfall intensity of each effective two-dimensional surface grid based on the area of ​​each effective two-dimensional surface grid in the area to be studied, the Gaussian spatial weight of each effective two-dimensional surface grid, and the total area of ​​all effective two-dimensional surface grids in the area to be studied.

[0088] In one possible implementation, the Gaussian spatial weights of each effective two-dimensional surface grid are determined based on a two-dimensional Gaussian moving attenuation rainfall field.

[0089] In one possible implementation, the two-dimensional Gaussian moving attenuation rainfall field includes a peak intensity term and a spatial attenuation term; The peak intensity term is a sequence of peak intensity changes over time based on the center of the rain cluster, determining the instantaneous peak intensity of the rain cluster center at each moment; the spatial attenuation term is based on the Gaussian distribution function, determining the spatial distribution characteristics of the rainfall intensity at any location in the study area as the distance from the rainfall center increases.

[0090] Figure 13 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 13 As shown, the electronic device 13 of this embodiment includes a processor 130 and a memory 131. The memory 131 stores a computer program 132. When the processor 130 executes the computer program 132, it implements the steps in the various method embodiments described above. Alternatively, when the processor 130 executes the computer program 132, it implements the functions of each module / unit in the various device embodiments described above.

[0091] For example, computer program 132 may be divided into one or more modules / units, which are stored in memory 131 and executed by processor 130 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 132 in electronic device 13.

[0092] Electronic device 13 may include, but is not limited to, processor 130 and memory 131. Those skilled in the art will understand that... Figure 13 This is merely an example of electronic device 13 and does not constitute a limitation on electronic device 13. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 13 may also include input / output devices, network access devices, buses, etc.

[0093] The processor 130 can be a central processing unit (CPU), or 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 can be a microprocessor or any conventional processor.

[0094] The memory 131 can be an internal storage unit of the electronic device 13, such as a hard disk or RAM. The memory 131 can also be an external storage device of the electronic device 13, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 131 can include both internal and external storage units of the electronic device 13. The memory 131 is used to store the computer program 132 and other programs and data required by the electronic device 13. The memory 131 can also be used to temporarily store data that has been output or will be output.

[0095] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0096] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0097] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0098] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0099] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0100] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for simulating urban flooding, characterized in that, include: Acquire basic data for the area to be studied, including data on the area boundary, digital elevation model, land use type, drainage network, and underlying surface of the area to be studied. Based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuation rainfall field, the instantaneous rainfall intensity at any location in the area under study is determined. The two-dimensional Gaussian moving attenuation rainfall field includes a peak intensity term and a spatial attenuation term. The peak intensity term is based on the sequence of peak intensity changes over time at the center of the rain cluster to determine the instantaneous peak intensity at each moment. The spatial attenuation term is based on a Gaussian distribution function to determine the spatial distribution characteristics of the rainfall intensity at any location in the area under study as the distance from the rainfall center increases. The instantaneous rainfall intensity at any location in the area under study is mapped to a two-dimensional surface grid, and the net rainfall of each two-dimensional surface grid is calculated. The net rainfall of each two-dimensional surface grid is used as the surface source term of the two-dimensional surface hydrodynamic model. The two-dimensional surface hydrodynamic model and the one-dimensional drainage network model are coupled and calculated to obtain the hydrodynamic calculation results at each time point. The two-dimensional surface hydrodynamic model and the one-dimensional drainage network model are constructed based on the basic data of the area under study. Based on the hydrodynamic calculation results at each time point, the spatiotemporal evolution of waterlogging in the area under study is carried out.

2. The urban flooding simulation method according to claim 1, characterized in that, The step of determining the instantaneous rainfall intensity at any location in the area under study based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuation rainfall field includes: Based on the preset rainfall process, determine the sequence of the peak intensity of the rain cluster center changing over time; Based on the set rain cluster movement velocity vector, the initial center position of the rain cluster, and the spatial scale parameters of the rain cluster, the center position of the rain cluster is updated, and based on the two-dimensional Gaussian moving attenuation rainfall field, the instantaneous rainfall intensity of the area under study at any location is determined.

3. The urban flooding simulation method according to claim 1, characterized in that, Before determining the instantaneous rainfall intensity at any location in the area under study, the method further includes: Based on the area of ​​each effective two-dimensional surface grid in the study area, the Gaussian spatial weight of each effective two-dimensional surface grid, and the total area of ​​all effective two-dimensional surface grids in the study area, the rainfall intensity of each effective two-dimensional surface grid is normalized.

4. The urban flooding simulation method according to claim 3, characterized in that, The Gaussian spatial weights of each effective two-dimensional surface grid are determined based on the two-dimensional Gaussian moving attenuation rainfall field.

5. The urban flooding simulation method according to any one of claims 1-4, characterized in that, The process of mapping the instantaneous rainfall intensity at any location in the area under study to a two-dimensional surface grid and calculating the net rainfall of each two-dimensional surface grid includes: Based on the scale of the two-dimensional surface grid corresponding to any location and the spatial scale of the rain cluster, the rainfall intensity of each two-dimensional surface grid is determined. Based on the land use type corresponding to each two-dimensional surface grid, the rainfall loss intensity of each two-dimensional surface grid is determined; The net rainfall intensity of each two-dimensional surface grid is determined based on the difference between the rainfall intensity of each two-dimensional surface grid and the rainfall loss intensity of each two-dimensional surface grid. Based on the net rainfall intensity of each two-dimensional surface grid, the net rainfall amount of each two-dimensional surface grid is calculated.

6. The urban flooding simulation method according to any one of claims 1-4, characterized in that, The hydrodynamic calculation results at each time point include surface water depth, flow velocity, pipeline head, and pipeline flow rate; The spatiotemporal evolution of waterlogging in the area under study, based on the hydrodynamic calculation results at each time point, includes: Based on the hydrodynamic calculation results at each time point, the water accumulation range, peak water depth, peak occurrence time, high-risk initiation time, high-risk duration, and receding process of the area under study are determined. Based on the water accumulation range, peak water depth, peak occurrence time, high-risk initiation time, high-risk duration, and receding process of the area under study, the spatiotemporal evolution of urban flooding in the area under study is analyzed.

7. An urban flooding simulation device, characterized in that, include: The acquisition module is used to acquire basic data of the area to be studied, wherein the basic data includes data on the area boundary, digital elevation model, land use type, drainage network and underlying surface of the area to be studied; The first determining module is used to determine the instantaneous rainfall intensity at any location in the area under study based on a preset rainfall process and a preset two-dimensional Gaussian moving attenuation rainfall field. The two-dimensional Gaussian moving attenuation rainfall field includes a peak intensity term and a spatial attenuation term. The peak intensity term is based on the sequence of peak intensity changes over time at the center of the rain cluster to determine the instantaneous peak intensity at each moment. The spatial attenuation term is based on a Gaussian distribution function to determine the spatial distribution characteristics of the rainfall intensity at any location in the area under study as the distance from the rainfall center increases. The calculation module is used to map the instantaneous rainfall intensity at any location in the area under study to a two-dimensional surface grid, and calculate the net rainfall of each two-dimensional surface grid. The second determining module is used to take the net rainfall of each two-dimensional surface grid as the surface source term of the two-dimensional surface hydrodynamic model, and perform coupled calculations of the two-dimensional surface hydrodynamic model and the one-dimensional drainage network model to obtain the hydrodynamic calculation results at each time point; wherein, the two-dimensional surface hydrodynamic model and the one-dimensional drainage network model are constructed based on the basic data of the area to be studied. The evolution module is used to perform spatiotemporal evolution of waterlogging in the area under study based on the hydrodynamic calculation results at each time point.

8. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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