Urban inland inundation disaster spatial distribution characteristic simulation analysis method

Abstract

The invention relates to the technical field of urban inland inundation analysis, in particular to an urban inland inundation disaster spatial distribution characteristic simulation analysis method. According to the method, basic information of a to-be-analyzed city is investigated and collected, rainstorm waterlogging surface hydrological factors are extracted based on the collected basic information of the city, then a city waterlogging simulation analysis model is established based on the collected basic information of the city and the extracted rainstorm waterlogging surface hydrological factors, and then the rainfall and the rainfall pattern in the recurrence period are determined. According to the method, the established urban inland inundation simulation analysis model is refined to obtain an optimized urban inland inundation simulation analysis model, and the optimized urban inland inundation simulation analysis model is utilized to perform inland inundation disaster space distribution characteristic analysis on a to-be-analyzed city, so that the urban inland inundation disaster space distribution characteristic analysis of the to-be-analyzed city can be accurately predicted when an extra-large rainfall event occurs in the extreme weather. And urban inland inundation disaster spatial distribution characteristics are obtained, so that the influence degree of future disasters can be predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of urban waterlogging analysis, and in particular to a method for simulating and analyzing spatial distribution characteristics of urban waterlogging disasters. Background Art

[0002] Flood disasters are one of the most common and most serious natural disasters in China. The number of deaths, the number of people affected and the economic losses caused by flood disasters all rank first in the ranking of natural disaster events. In recent years, with the development of society, urban expansion and the continuous increase in urban area, the hydrological effect brought about by urbanization has increased the frequency of urban flood disasters. Urban waterlogging disasters have become complex, diverse and chained, causing serious social and economic losses.

[0003] Although most modern cities have solved the problem of flooding under normal circumstances through the adjustment and dredging of river systems and the construction of underground pipelines, they still face the challenge of heavy precipitation events under extreme weather conditions. Since extreme weather has occurred less frequently in history and there is less accumulated empirical data, coupled with the adjustment of water systems and pipelines in cities, it is easy to cause uncertainty in the consequences of heavy precipitation. Without the support of a large amount of data and scientific models, it is difficult to predict the impact of future disasters. Summary of the invention

[0004] The purpose of the present invention is to provide a method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters, so as to solve the problem that due to the low frequency of extreme weather in history, the accumulated empirical data is small, and the adjustment of water systems and pipelines in cities easily causes uncertainty in the consequences of heavy rainfall. Without the support of a large amount of data and scientific models, it is difficult to predict the impact of future disasters.

[0005] To achieve the above object, the present invention provides a method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters, comprising the following steps:

[0006] S1: Investigate and collect basic information of the city to be analyzed;

[0007] S2: Extracting surface hydrological factors of rainstorm waterlogging based on the collected basic information of the city;

[0008] S3: Establish an urban waterlogging simulation analysis model based on the collected basic urban information and the extracted surface hydrological factors of rainstorm waterlogging;

[0009] S4: Based on the existing observation data, the Gumbel distribution function is used to calculate the rainstorm rainfall under 7 return periods, and the return period rainfall is obtained. The rainfall data of the meteorological stations are used to statistically analyze the rainfall types of the rainstorm weather processes of different magnitudes in the region;

[0010] S5: Based on the determination of the return period rainfall and rainfall type, the urban waterlogging simulation analysis model is refined to obtain an optimized urban waterlogging simulation analysis model;

[0011] S6: Use the optimized urban waterlogging simulation analysis model to analyze the spatial distribution characteristics of waterlogging disasters in the analyzed city.

[0012] Among them, in step S1, the basic information that needs to be collected for the city to be analyzed includes geographical information, basic urban construction conditions, meteorological and hydrological data, and potential waterlogging risk points.

[0013] Among them, in step S2, the extraction of surface hydrological factors of rainstorm waterlogging includes surface hydrological factor extraction and surface hydrological analysis. The surface hydrological factor extraction includes slope extraction, aspect extraction and watershed extraction. The surface hydrological analysis includes DEM depression filling preprocessing, DEM-based water flow direction extraction and water flow direction-based runoff accumulation calculation.

[0014] Among them, in step S3, the specific steps of establishing the urban waterlogging simulation analysis model include surface runoff calculation, infiltration water calculation, and hydraulic process calculation.

[0015] Among them, in step S3, surface runoff includes surface flow generation process and surface runoff convergence process. The surface runoff generation is calculated by nonlinear reservoir method, and multiple catchment areas are regarded as independent reservoirs, and the solution is obtained by simultaneous continuity equation and Manning equation. The surface runoff convergence process is the process of discharge from the drainage outlet of the basin after the surface canal and urban drainage network system dredge, converge and drain the flow generated in the catchment area. The calculation method of the network drainage volume is based on Manning formula.

[0016] Wherein, in step S3, the infiltration water volume is calculated using the Horton infiltration calculation equation and the runoff curve numerical method.

[0017] Among them, in step S3, hydraulic process calculation specifically refers to calculating the movement of water flow in the pipeline through three calculation methods: steady flow method, motion wave method and dynamic wave method.

[0018] Among them, in step S3, the specific steps of establishing the urban waterlogging simulation analysis model also include watershed division, pipe network generalization and model parameter calibration.

[0019] The present invention provides a method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters. The method comprises the following steps: investigating and collecting basic information of the city to be analyzed, extracting surface hydrological factors of rainstorm and waterlogging based on the collected basic information of the city, and then establishing an urban waterlogging simulation analysis model based on the collected basic information of the city and the extracted surface hydrological factors of rainstorm and waterlogging. Then, by determining the rainfall and rain type during the return period, the established urban waterlogging simulation analysis model is refined to obtain an optimized urban waterlogging simulation analysis model. The optimized urban waterlogging simulation analysis model is used to analyze the spatial distribution characteristics of waterlogging disasters in the city to be analyzed, thereby accurately predicting the spatial distribution characteristics of urban waterlogging disasters when a heavy precipitation event occurs in the city under extreme weather conditions, thereby predicting the impact of future disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of the steps of the method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters provided by the present invention.

[0022] Figure 2 It is a flow chart of the water catchment extraction technology provided by the present invention.

[0023] Figure 3 This is a working principle diagram of the depression filling provided by the present invention.

[0024] Figure 4 It is a working principle diagram of the water flow direction provided by the present invention.

[0025] Figure 5 It is a working principle diagram of the current accumulation provided by the present invention. DETAILED DESCRIPTION

[0026] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0027] See also Figures 1 to 5 The present invention provides a method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters, comprising the following steps:

[0028] S1: Investigate and collect basic information of the city to be analyzed;

[0029] S2: Extracting surface hydrological factors of rainstorm waterlogging based on the collected basic information of the city;

[0030] S3: Establish an urban waterlogging simulation analysis model based on the collected basic urban information and the extracted surface hydrological factors of rainstorm waterlogging;

[0031] S4: Based on the existing observation data, the Gumbel distribution function is used to calculate the rainstorm rainfall under 7 return periods, and the return period rainfall is obtained. The rainfall data of the meteorological stations are used to statistically analyze the rainfall types of the rainstorm weather processes of different magnitudes in the region;

[0032] S5: Based on the determination of the return period rainfall and rainfall type, the urban waterlogging simulation analysis model is refined to obtain an optimized urban waterlogging simulation analysis model;

[0033] S6: Use the optimized urban waterlogging simulation analysis model to analyze the spatial distribution characteristics of waterlogging disasters in the analyzed city.

[0034] In this embodiment, the basic information of the city to be analyzed is investigated and collected, and the surface hydrological factors of rainstorm and waterlogging are extracted based on the collected basic information of the city. Then, an urban waterlogging simulation analysis model is established based on the collected basic information of the city and the extracted surface hydrological factors of rainstorm and waterlogging. Then, by determining the rainfall and rain type during the return period, the established urban waterlogging simulation analysis model is refined to obtain an optimized urban waterlogging simulation analysis model. The optimized urban waterlogging simulation analysis model is used to analyze the spatial distribution characteristics of waterlogging disasters in the city to be analyzed, so as to accurately predict the spatial distribution characteristics of urban waterlogging disasters when heavy precipitation events occur in extreme weather conditions in the city, thereby predicting the impact of future disasters.

[0035] Furthermore, in step S1, the basic information that needs to be collected for the city to be analyzed includes geographic information, basic urban construction conditions, meteorological and hydrological data, and potential waterlogging risk points.

[0036] The survey and collection of geographic information are as follows:

[0037] Collect city research zoning maps larger than 1:250,000;

[0038] Collect 1:50,000 water system maps of cities;

[0039] Collect 1:10,000 topographic maps of the city;

[0040] Collect 1:50,000 land use maps of cities;

[0041] Collect high-resolution remote sensing image data of the city;

[0042] Collect high-precision DEM of the city;

[0043] Collect other basic geographic information related data: mainly including watershed boundaries, transportation and infrastructure, and the distribution of potential waterlogging risk points.

[0044] Among them, the basic situation survey and collection of urban construction is specifically as follows:

[0045] Urban population distribution and socioeconomic conditions;

[0046] Urban water conservancy projects, including embankments, lakes, ditches, urban rainwater pipe networks, drainage facilities, etc.;

[0047] River channel distribution and cross-section information, water flow trends;

[0048] Urban planning, including land use planning, road planning, building complex planning, water supply and drainage planning, etc.;

[0049] Drainage design capacity of flood-prone areas;

[0050] Investigate the status of river channel cleaning and improvement, underground pipeline laying, urban greening or hardening construction, and other previous work related to urban waterlogging.

[0051] The specific survey and collection of meteorological and hydrological data include:

[0052] Collect hourly rainfall data from existing weather stations in the city (including regional stations);

[0053] Collect hourly rainfall and hydrological data from existing hydrological (water level, rainfall) stations in the city;

[0054] Collect existing hydrological data.

[0055] Among them, the information investigation and collection of urban waterlogging risk points is specifically: investigating the urban waterlogging inundation situation, including the name, location, scope, inundation depth, and duration of the inundation point.

[0056] Furthermore, in step S2, the extraction of surface hydrological factors of rainstorm waterlogging includes surface hydrological factor extraction and surface hydrological analysis, wherein the surface hydrological factor extraction includes slope extraction, aspect extraction and watershed extraction, and the surface hydrological analysis includes DEM depression filling preprocessing, DEM-based water flow direction extraction and water flow direction-based runoff accumulation calculation.

[0057] The catchment area refers to a terrain unit where rainwater converges and drains. The study area is divided into catchment areas by extracting the flow direction, cumulative runoff, flow length, and flow network of surface runoff. The extraction of these basic hydrological factors and basic hydrological analysis provide a theoretical basis and data foundation for the final hydrological analysis and numerical simulation of waterlogging. The technical process of watershed extraction is as follows: Figure 2 As shown:

[0058] Among them, DEM depression filling preprocessing specifically refers to: the urban surface is very different from the surface of a general river basin. The urban surface is made of cement, stone, brick, asphalt, concrete, etc., and the landscape is made of greenery, buildings, trees, etc., as well as various loose lands scattered everywhere, which are relatively mixed. This makes the DEM surface of the city have concave grids. When simulating surface water flow in these areas, the calculation of the water flow direction in the area will be biased or unreasonable. Therefore, before the calculation, the original DEM should be preprocessed. In addition to the conventional DEM preprocessing, a very important preprocessing task is depression filling. The working principle of depression filling, the depth of the depression and the results after DEM filling are as follows: Figure 3 shown.

[0059] Among them, the water flow direction extraction based on DEM specifically refers to: In DEM, the water flow direction refers to the direction of the water flow when it leaves each grid unit. One of the keys to obtaining the surface hydrological characteristics of the study area is to be able to determine the direction of outflow from each pixel in the grid. In the eight-direction method (D8) flow direction modeling, it is generally believed that there are eight effective output directions, which are related to the eight adjacent pixels where the flow can flow into. Its theoretical basis and the results of water flow direction and water flow length are as follows Figure 4 shown.

[0060] The calculation of runoff accumulation based on water flow direction specifically refers to: in the process of surface runoff simulation, the runoff accumulation is calculated from the water flow direction. For each grid, the size of its runoff accumulation indicates how many grids upstream have water flow directions that eventually converge through the grid. The larger the runoff accumulation value, the easier it is for the area to form surface runoff. The working principle of the runoff accumulation based on water flow direction and the calculation of the runoff accumulation in the study area are as follows: Figure 5 shown.

[0061] Furthermore, in step S3, the specific steps of establishing the urban waterlogging simulation analysis model include surface runoff calculation, infiltration water calculation, and hydraulic process calculation.

[0062] The surface runoff calculation specifically refers to:

[0063] Surface runoff includes the process of surface runoff generation and convergence. Runoff generation refers to the flow of water generated on the surface by rainfall through plant interception, infiltration, evaporation and other processes. The surface runoff calculation adopts the nonlinear reservoir method, that is, each catchment area is regarded as an independent reservoir, and is solved by simultaneously solving the continuity equation and the Manning equation. Surface convergence refers to the process in which the runoff generated after rainfall is gathered to the outlet of the basin and flows out. The convergence process is greatly affected by the underlying surface, and the convergence capacity of different underlying surfaces is different. The urban underlying surface is complex, with large differences between regions, and most of them are impermeable ground. The input items of the sub-catchment area include: rainfall; the outflow items include: infiltration water, evaporation water and outflow water, etc. When the water storage depth exceeds the maximum depression storage depth ds, overflow will occur on the surface. The calculation formula is as follows:

[0064]

[0065] Where V is the total water storage capacity (unit: m3), V = A × d; A is the subcatchment area (unit: m2); t is the time (unit: s); d is the water depth (unit: m); i* is the net rainfall (unit: m); Q is the runoff flow (unit: m3);

[0066] in,

[0067] Where W is the overflow width of the catchment area; n is the surface Manning roughness coefficient; dp is the surface storage depth; S is the width of the catchment area. Combining Formula 1 and Formula 2 to simplify, we can get the nonlinear differential equation of water depth d:

[0068]

[0069] The overflow width W, slope S, and roughness n of the catchment area are used to calculate the parameter WCON. The equation is solved using the finite difference method. The values ​​of net inflow and net outflow are the average values ​​within the time step. Formula 3 is processed and the results are as follows:

[0070]

[0071] In the formula, d1 and d2 represent the initial water depth and the final water depth respectively. The New-Raphson iteration method is used to solve the problem and obtain:

[0072] F = Δd-Δt(WCONβ 5 / 3 +i) Formula 5

[0073] Where F is the Newton function,

[0074] Differentiate the Newton function:

[0075] Finally, we get the recursive function of Δd:

[0076] The above method can calculate the outflow at the end of the time step;

[0077] Confluence refers to the process of discharge from the drainage outlet of the basin after the surface canal and urban drainage network system have been used to drain, collect and drain the flow in the catchment area. The calculation method of the drainage volume of the network is based on the Manning formula. The drainage volume of the network is Q p It is determined by the drainage capacity of the pipe network and the drainage duration, and the calculation form is as follows:

[0078]

[0079] In formula 9, n is the roughness of the pipe wall; d is the pipe diameter (unit: m); S is the slope of the pipe bottom (the ratio of the height difference between the pipe ends to the projected length of the pipe in the horizontal direction); Δt is the drainage duration.

[0080] The infiltration water volume is calculated using the Horton infiltration equation and the runoff curve numerical method.

[0081] The Horton equation is an empirical equation. Its principle is that during a rainfall event, the infiltration attenuation index decreases from the initial maximum infiltration rate to a certain minimum value. The input parameters include: maximum and minimum infiltration rates, attenuation coefficient, and the time required for the soil layer to go from full saturation to full dryness. The calculation equation is as follows:

[0082] q 下渗 =I t *Δt*S,I t =f o +(f i -f o ) -αt Formula 10

[0083] In the formula, I t is the average infiltration rate (unit: mm / s) within Δt (time interval, unit: s); S is the permeable area (unit: m2); f o is the final infiltration rate (unit: mm / s); f i is the initial infiltration rate (unit: mm / s); α is the infiltration decrease rate (unit: mm / s).

[0084] Numerical method of runoff curve:

[0085] The SCS numerical curve is listed in the book "Study on SCS in Small Watersheds - Taking Urban Hydrology as an Example" (1986). This method is evolved from the method of calculating the runoff NRCS (SCS) digital curve. It is assumed that the total infiltration capacity of the soil can be obtained from the soil (water content) numerical curve. In a rainfall event, the infiltration capacity decreases with the increase of rainfall and soil water retention. The calculation equation is as follows:

[0086]

[0087] Where Q is runoff; S is soil water suction; I is rainfall; CN is the numerical curve number.

[0088] According to the research results of the U.S. Soil and Water Conservation Department, soils are divided into four groups: A, B, C, and D according to soil pore characteristics. The CN value of each type of soil can be found in the table, as shown in Table 1:

[0089] Table 1. SCS runoff numerical curve method CN value comparison table

[0090]

[0091]

[0092] Among them, hydraulic process calculation specifically refers to the calculation of the movement of water flow in the pipeline through three calculation methods: steady flow method, motion wave method and dynamic wave method.

[0093] The dynamic wave method is the most accurate in theory because it solves the complete Saint-Venant equations for confluence routing, which include the continuous momentum equations in the conduit and the mass conservation equations at the nodes.

[0094] The dynamic wave method can calculate the pressurized flow when the closed conduit is full. When full, the water flow can exceed the conduit load. When overflow occurs, the water depth at the node exceeds the maximum water storage depth of the node. At this time, the excess water is either lost directly from the system or stored at the previous assembly point, waiting to be re-introduced into the drainage system when the conduit water load decreases.

[0095] The mass conservation equation and the dynamic conservation equation can be used to solve the water flow in the pipe network. The equation system is as follows:

[0096]

[0097]

[0098] The energy gradient due to friction is calculated using the Manning formula:

[0099]

[0100] K=gn 2 Formula 17

[0101] Will

[0102] Substituting into the above equations, we get:

[0103]

[0104] Combining the above equations, we get the basic flow equation:

[0105]

[0106] Based on the above equations, the flow rate of each pipeline and the water head of each node in each period can be solved in turn, which can be expressed in the form of finite differences as follows:

[0107]

[0108] In the formula, are the weighted average values ​​at the end of the pipe at time t. In addition, to consider the inlet and outlet losses of the pipe, the head loss can be subtracted from H1 and H2. The main unknown in the formula is Q t+Δt , H1, H2, variables They are all related to Q and H. Therefore, we also need equations related to Q and H, which can be obtained from the node equations.

[0109] The dynamic wave node control equation is written in finite difference form as:

[0110]

[0111] To find the flow rate of each connection section and the water head of each node within the time period Δt, it is only necessary to solve the above set of equations.

[0112] Furthermore, in step S3, the specific steps of establishing the urban waterlogging simulation analysis model also include watershed division, pipe network generalization and model parameter calibration.

[0113] Among them, the watershed division specifically refers to: the watershed, also known as the catchment area, refers to the surface area through which runoff flows from the surface to the outlet. It is the basic evaluation unit of the hydrological analysis model. Different division methods will have different effects on the simulation results of the model. This paper will use DEM data to comprehensively analyze the distribution of the city's main roads, main rivers, inspection wells, drainage outlets, etc., and divide the watershed area of ​​the study area.

[0114] The watershed division is realized according to the following methods: first, based on the meteorological station and its longitude and latitude information, Thiessen polygons are established based on the meteorological station, which is used as the watershed division reflecting the distribution of the urban drainage system; second, based on the digital elevation (DEM) data, hydrological analysis is carried out in GIS, and the three functions of depression filling, slope aspect, and sub-basin extraction are used to obtain the watershed division reflecting the natural landform of the city; third, on the basis of the Thiessen polygon watershed, the natural watershed is adjusted so that the watershed reflects both the urban landform and the influence of the drainage system, thereby forming a refined urban watershed division.

[0115] On the basis of determining the urban watershed, GIS statistical analysis tools are used to calculate the generalized parameters of the watershed, including the area of ​​each watershed, impervious area, impervious ratio, average slope of the watershed, etc.

[0116] Pipeline network generalization specifically means: because the actual drainage network is often not conducive to simulation and calculation due to factors such as data limitations and model complexity, the actual drainage network system is abstracted and generalized for analysis. Due to the lack of drainage network data in this project, the pipe network and nodes used in the model simulation process are all generated by the model.

[0117] The model parameter calibration is as follows: GIS is combined with the urban stormwater model to conduct urban waterlogging analysis, and the key task is the model parameter calibration.

[0118] The parameters involved in the model can be roughly divided into two categories: one is non-calibrated parameters, which can be obtained directly through data attributes or through GIS analysis and calculation, such as the catchment area, average slope of the catchment area, impervious area of ​​the catchment area and other parameters; the other is calibrated parameters, which are difficult to obtain through measured data, but the corresponding value ranges have been given in industry standards and manual specifications. It is necessary to calibrate the model parameters in combination with field conditions and investigations and studies to determine their optimal values, such as the Manning coefficient of the permeable area, the Manning coefficient of the impervious area and the maximum infiltration rate.

[0119] Experience parameters:

[0120] The empirical parameters are the parameters that need to be calibrated. This project mainly calibrates 7 parameters, namely N-Imperv, N-Perv, MaxRate, D-Imperv, Decay, D-Perv and MinRate. The range of the calibrated parameters can be determined according to the drainage manual and model specifications. The range of the calibrated parameters is as follows:

[0121] Table 2. Range of values ​​of calibration parameters

[0122]

[0123] Rating method:

[0124] At present, there are two commonly used calibration methods for hydrological models: human-computer interaction method and automatic method. Human-computer interaction calibration is a common method for parameter calibration. By setting a set of parameter values, comparing the simulated value and the measured value and adjusting the parameters until the parameters reach the optimal value, this value is used as the result value of the calibration parameters. Automatic calibration means that before determining the calibration parameter value, an initial value is assigned, and under a pre-programmed program, the objective function of the optimization calibration is established, and the optimal solution of the parameters is obtained through automatic calibration of the algorithm, such as genetic variation method, particle swarm algorithm, etc.

[0125] The measured rainfall data and outlet flow data are used to calibrate and optimize the required parameters. The simulation results of the model are compared and analyzed with the measured data by establishing an objective function to determine the values ​​of the model calibration parameters.

[0126] The land use type classification results are based on remote sensing images, and the parameters of each land use type are calibrated with reference to the SWMM user manual. The watershed is taken as the basic evaluation unit, and the weighted average of the areas of various land features in the watershed is used to determine the Manning coefficient in the watershed.

[0127] Table 3. Manning coefficients for different land use types

[0128]

[0129]

[0130] Further, in step S5, in determining the return period rainfall, the return period (CDF) refers to the return period of the rainstorm intensity, which refers to the average interval time during which a rainstorm intensity greater than or equal to this value may occur once, and the unit is year (a).

[0131] The calculation method of the return period is generally based on the existing observation data, using mathematical statistics methods, and fitting the historical observation meteorological element values ​​with a certain distribution method to form a distribution curve of the meteorological element occurrence probability P and the element value x. According to a certain occurrence probability P, the corresponding meteorological element value x can be obtained. This method can be used to obtain the corresponding meteorological element value that occurs once in many years. This project uses the Gumbel distribution function to calculate the rainstorm rainfall under 7 return periods (5 years, 10 years, 15 years, 20 years, 30 years, 50 years and 100 years).

[0132] The rain type of a rainstorm process is specifically determined as follows: the change of rainfall intensity over time in a rainstorm process is called a rain type. The rain type is a description of the statistical relationship of rainfall time distribution. For the same rainstorm process, different rain types are selected for rainfall allocation and drainage state simulation, which will lead to different drainage network load states. The rain types of rainstorm weather processes of different magnitudes in the region are statistically analyzed based on the rainfall data of the meteorological station.

[0133] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters, characterized in that: The steps include: S1: Investigate and collect basic information of the city to be analyzed; S2: Extracting surface hydrological factors of rainstorm waterlogging based on the collected basic information of the city; S3: Establish an urban waterlogging simulation analysis model based on the collected basic urban information and the extracted surface hydrological factors of rainstorm waterlogging; S4: Based on the existing observation data, the Gumbel distribution function is used to calculate the rainstorm rainfall under 7 return periods, and the return period rainfall is obtained. The rainfall data of the meteorological stations are used to statistically analyze the rainfall types of the rainstorm weather processes of different magnitudes in the region; S5: Based on the determination of the return period rainfall and rainfall type, the urban waterlogging simulation analysis model is refined to obtain an optimized urban waterlogging simulation analysis model; S6: Use the optimized urban waterlogging simulation analysis model to analyze the spatial distribution characteristics of waterlogging disasters in the analyzed city.

2. The method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters according to claim 1, characterized in that: In step S1, the basic information that needs to be collected for the city to be analyzed includes geographic information, basic urban construction conditions, meteorological and hydrological data, and potential waterlogging risk points.

3. The method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters according to claim 2, characterized in that: In step S2, the extraction of surface hydrological factors of rainstorm waterlogging includes surface hydrological factor extraction and surface hydrological analysis. The surface hydrological factor extraction includes slope extraction, aspect extraction and watershed extraction. The surface hydrological analysis includes DEM depression filling preprocessing, DEM-based water flow direction extraction and water flow direction-based runoff accumulation calculation.

4. The method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters according to claim 3, characterized in that: In step S3, the specific steps of establishing the urban waterlogging simulation analysis model include surface runoff calculation, infiltration water calculation, and hydraulic process calculation.

5. The method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters according to claim 4, characterized in that: In step S3, surface runoff includes surface runoff generation and surface runoff convergence. The surface runoff generation is calculated using a nonlinear reservoir method, with multiple catchment areas treated as independent reservoirs, and solved by simultaneously solving the continuity equation and the Manning equation. The surface runoff convergence process is the process of discharge from the drainage outlet of the basin after the surface canal and the urban drainage network system drain, converge and drain the runoff generated in the catchment area. The calculation method of the network drainage volume is based on the Manning formula.

6. The method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters according to claim 5, characterized in that: In step S3, the infiltration water volume is calculated using the Horton infiltration calculation equation and the runoff curve numerical method.

7. The method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters according to claim 6, characterized in that: In step S3, hydraulic process calculation specifically refers to calculating the movement of water flow in the pipeline through three calculation methods: steady flow method, motion wave method and dynamic wave method.

8. The method for simulating and analyzing the spatial distribution characteristics of urban waterlogging disasters according to claim 7, characterized in that: In step S3, the specific steps of establishing the urban waterlogging simulation analysis model also include watershed division, pipe network generalization and model parameter calibration.

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