A high-precision urban flood prediction method taking into account the uneven temporal and spatial distribution of rainfall

Abstract

The present invention provides a high-precision urban flood prediction method that takes into account the uneven temporal and spatial distribution of rainfall, and relates to the field of flood prevention technology. The method comprises the following steps: obtaining rainwater pipe network data, building outline data, land use type data and meteorological data of a target area respectively; constructing a precipitation-runoff simulation model based on the rainwater pipe network data and the building outline data; obtaining time series data and rainfall data by analyzing the time resolution and time span based on the meteorological data; constructing a two-dimensional surface hydrodynamic model based on the building outline data, rainfall data and land use type data; inputting the time series data into the precipitation-runoff simulation model, and performing flow exchange calculation with the two-dimensional surface hydrodynamic model based on the boundary conditions of the two-dimensional surface hydrodynamic model to obtain an urban flood prediction result, thereby completing a high-precision prediction of urban floods. The present invention solves the problem that the precision of urban flood simulation is not high.

Description

Technical Field

[0001] The present specification relates to the field of flood prevention technology, and in particular to a high-precision urban flood prediction method that takes into account the uneven temporal and spatial distribution of rainfall. Background Art

[0002] The rapid expansion of urban scale has brought about complex changes in the underlying surface, which is mainly manifested in the reduction of urban storm water storage space, the expansion of impervious areas, and the reduction of surface infiltration, which has increased runoff. In addition, studies have shown that global warming in recent years has led to increased evaporation of water in the atmosphere, thereby increasing the intensity, frequency and duration of extreme rainstorm events, which will lead to greater rainstorms and peak flows, exacerbating the risk of urban waterlogging.

[0003] In order to cope with the potential threats brought by increasingly frequent urban flood events, exploring accurate, fast and stable urban flood modeling methods is crucial for preventing and mitigating the impact of flood disasters. High-precision simulation results can also provide key information for urban management. Flood models are divided into one-dimensional, two-dimensional models and one- and two-dimensional coupled models. One-dimensional models represented by SWMM can only simulate the flow changes in urban drainage networks, but cannot simulate the flow of floods on the ground, the scope of inundation and the depth of water. The two-dimensional surface hydrodynamic models represented by LISFLOOD-FP and HEC-RAS mainly calculate complex physical models, such as two-dimensional shallow water equations. Although the two-dimensional model can simulate the inundation range of urban surface floods well, the impact of the dense drainage system in the city cannot be ignored. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a high-precision urban flood prediction method taking into account the uneven temporal and spatial distribution of rainfall, which solves the problem of low precision in urban flood simulation.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a high-precision urban flood prediction method taking into account the uneven temporal and spatial distribution of rainfall, comprising:

[0006] S1: Obtain the rainwater pipe network data, building outline data, land use type data and meteorological data of the target area respectively;

[0007] S2: constructing a precipitation-runoff simulation model based on the rainwater pipe network data and the building outline data;

[0008] S3: Based on the meteorological data, obtaining time series data and rainfall data by analyzing time resolution and time span;

[0009] S4: constructing input data of a two-dimensional surface hydrodynamic model based on the building outline data, the rainfall data and the land use type data;

[0010] S5: Input the time series data into the precipitation-runoff simulation model, perform flow exchange calculation with the two-dimensional surface hydrodynamic model based on the boundary conditions of the two-dimensional surface hydrodynamic model, obtain urban flood prediction results, and complete high-precision prediction of urban floods.

[0011] Furthermore, the S1 includes:

[0012] Obtain stormwater network data, building outline data, land use type data and digital elevation model of the target area;

[0013] The meteorological data is obtained by analyzing the monitoring data of the meteorological department and using the digital elevation model; wherein the meteorological data includes a three-dimensional array of rainfall.

[0014] Further, the S2 includes:

[0015] Generate building surface data based on the building outline data;

[0016] A precipitation-runoff simulation model is constructed using stormwater pipe network data and building surface data.

[0017] Further, the S3 includes:

[0018] Convert the building surface data into a new format to obtain building surface raster data;

[0019] Based on the three-dimensional rainfall data and the building surface grid data, time resolution and time span analysis is performed to obtain the time series data and the rainfall data.

[0020] Further, the S5 includes:

[0021] S510: Inputting the time series data into the precipitation-runoff simulation model to obtain the coordinates of the rainwater inlet nodes;

[0022] S520: Based on the node coordinates of the rainwater inlet, obtaining the inflow of the node;

[0023] S530: establishing boundary conditions of a two-dimensional surface hydrodynamic model based on the inflow of the node;

[0024] S540: calling the precipitation-runoff simulation model and the two-dimensional surface water dynamics model in sequence within the same time interval, and obtaining the exchange flow of the precipitation-runoff simulation model and the two-dimensional surface water dynamics model through flow exchange calculation;

[0025] S550: superimposing the exchange flow and the inflow flow of the node to obtain an updated inflow flow of the node;

[0026] S560: looping from S520 to S550 within a preset time to obtain the urban flood prediction result finally output by the two-dimensional surface hydrodynamic model, thereby completing a high-precision prediction of urban floods.

[0027] The beneficial effects of the present invention are as follows: the processor uses the acquired rainwater pipe network data, building outline data, land use type data, digital elevation model and meteorological data to construct a precipitation-runoff simulation model and a two-dimensional surface water dynamic model to obtain urban flood prediction results. In this way, urban flood simulation with uneven rainfall distribution in time and space can be carried out; by using more detailed model input data, the precision of urban flood simulation is improved, making the simulation mechanism more consistent with the actual urban flood occurrence process; using Python and related libraries to couple the precipitation-runoff simulation model with the two-dimensional surface water dynamic model greatly simplifies the model coupling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0029] Figure 1 It is an exemplary flow chart of a high-precision urban flood prediction method that takes into account the uneven temporal and spatial distribution of rainfall according to some embodiments of this specification. DETAILED DESCRIPTION

[0030] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0031] Example

[0032] Figure 1 This is an exemplary flow chart of a high-precision urban flood prediction method that takes into account the uneven spatial and temporal distribution of rainfall according to some embodiments of this specification. Figure 1 As shown, the process includes the following steps. In some embodiments, the process can be executed by a processor.

[0033] S1: Obtain the rainwater pipe network data, building outline data, land use type data and meteorological data of the target area respectively.

[0034] Stormwater pipe network data is data that reflects the direction of rainwater flow in the environment. For example, stormwater pipe network data can include nodes such as inspection wells, stormwater inlets, and discharge outlets, as well as stormwater pipes used to connect nodes and corresponding elevation attributes.

[0035] The elevation attribute is the attribute data that reflects the altitude of the stormwater pipe network.

[0036] Building outline data is data reflecting the boundaries of buildings in the target area. For example, building outline data may include building size, direction, coordinates, shape, etc.

[0037] Land use type data is data reflecting the division of target areas by use. For example, land use type data may include forest land, grassland, commercial and service land, industrial and mining storage land, residential land, public management and public service land, etc.

[0038] Meteorological data is relevant data reflecting climate change in the target area. For example, meteorological data may include a three-dimensional array of rainfall, etc.

[0039] The first dimension of the rainfall three-dimensional array is the time dimension, and the second and third dimensions correspond to the grids of the digital elevation model. The value is the amount of rainfall (mm) that falls on the grid per unit time at the current time.

[0040] In some embodiments, the processor can obtain rainwater pipe network data, building outline data, land use type data and digital elevation model of the target area; obtain meteorological data by analyzing the monitoring data of the meteorological department and using the digital elevation model.

[0041] A digital elevation model is a digital model of the ground terrain using limited terrain elevation data. For example, a digital elevation model can include a simulated target area with ordered numerical arrays such as horizontal and vertical coordinates to represent the ground elevation, which is a physical ground model.

[0042] In some embodiments, the digital elevation model may reflect the terrain conditions of the simulated area, such as slope, aspect, and slope change rate.

[0043] In some embodiments, the processor may extract stormwater pipe network data, building outline data, land use type data, and a digital elevation model of a target area based on data provided by an urban planning department.

[0044] S2: Constructing a precipitation-runoff simulation model based on the rainwater pipe network data and the building outline data.

[0045] The precipitation-runoff simulation model (SWMM model) is a physical model used to simulate the water quantity and quality of a single precipitation event or a long-term event in a city.

[0046] In some embodiments, the processor may generate building surface data based on the building outline data; and construct a precipitation-runoff simulation model using the stormwater pipe network data and the building surface data.

[0047] Building surface data is data that reflects the area occupied by buildings.

[0048] In some embodiments, the processor may utilize the stormwater pipe network data and the building surface data to divide the target area and construct a precipitation-runoff simulation model.

[0049] In some embodiments, the processor may set the area where the building is located as a sub-catchment area of ​​the precipitation-runoff simulation model.

[0050] S3: Based on the meteorological data, time series data and rainfall data are obtained by analyzing time resolution and time span.

[0051] Time series data is data that reflects the average rainfall for each building over a period of time. For example, time series data can include the average rainfall for each period of time in the subcatchment area formed by the current building.

[0052] The rainfall data is data reflecting the rainfall time and rainfall amount at each coordinate.

[0053] In some embodiments, the processor may convert the format of the building surface data to obtain building surface raster data; perform time resolution and time span analysis based on the three-dimensional rainfall data and the building surface raster data to obtain the time series data and the rainfall data.

[0054] In some embodiments, the processor may perform format conversion on the building surface data to obtain building surface raster data in ASCII format:

[0055]

[0056] Among them, 1 means there is no building here, and 0 means there is a building here.

[0057] In some embodiments, the processor can perform time resolution and time span analysis on the first dimension of the three-dimensional rainfall data to obtain the shape and size of the first dimension, and compare the building surface raster data with the rainfall position in each time period in the three-dimensional rainfall data. If it is a building, the rainfall here is assigned a value of zero to obtain processed three-dimensional rainfall data.

[0058] In some embodiments, the processor may create the rainfall template via Python's netCDF4 library.

[0059] The rainfall template is a template file that reflects the coordinates, time, and rainfall of the target area. For example, the rainfall template may include the simulated target area horizontal coordinate, the simulated target area vertical coordinate, the time, and the rainfall dimension corresponding to the coordinate and time, wherein the value range and interval of the simulated target area horizontal coordinate and vertical coordinate need to be the same as the digital elevation model of the simulated area.

[0060] In some embodiments, the processor may input the processed three-dimensional rainfall data into a created rainfall template to obtain rainfall data.

[0061] In some embodiments, the processor may calculate the building surface grid data and the rainfall three-dimensional data to obtain the average rainfall of each building in each period of time as time series data:

[0062]

[0063] Among them, Q represents the average rainfall in each period of the sub-catchment area formed by the current building, X i Represents the rainfall of the current grid, i represents the i-th grid, and n represents the total number of grids.

[0064] S4: constructing input data of a two-dimensional surface hydrodynamic model based on the building outline data, the rainfall data and the land use type data.

[0065] In some embodiments, the processor can read building outline data, rainfall data, and land use type data, and obtain input data for a two-dimensional surface hydrodynamic model by constructing vectors.

[0066] S5: Input the time series data into the precipitation-runoff simulation model, perform flow exchange calculation with the two-dimensional surface hydrodynamic model based on the boundary conditions of the two-dimensional surface hydrodynamic model, obtain urban flood prediction results, and complete high-precision prediction of urban floods.

[0067] The boundary condition of the two-dimensional surface hydrodynamic model (LISFLOOD-FP model) is the time series of inflow and outflow flows at each time period of the stormwater inlet that exchanges flow with the precipitation-runoff model.

[0068] The urban flood prediction results reflect the flood inundation situation and inundation depth in the target area.

[0069] In some embodiments, the processor may implement S5 based on the following steps.

[0070] S510: Input the time series data into the precipitation-runoff simulation model to obtain the coordinates of the rainwater inlet nodes.

[0071] The gully node coordinates are the node coordinates that reflect the flow of rainwater into the pipe.

[0072] In some embodiments, the processor may input the time series data into a precipitation-runoff simulation model, analyze the rainfall in the sub-catchment area corresponding to each building, and obtain the coordinates and corresponding names of the stormwater inlet nodes.

[0073] S520: Based on the node coordinates of the rainwater inlet, obtain the inflow of the node.

[0074] The node inflow is the data of the amount of rainwater flowing into the stormwater inlet node changing with time.

[0075] In some embodiments, the processor may determine the inflow of the node based on the time series data and the coordinates of the stormwater inlet node.

[0076] S530: Establishing boundary conditions of a two-dimensional surface hydrodynamic model based on the inflow of the node.

[0077] In some embodiments, the processor may use the inflow of the node to update the boundary conditions of the two-dimensional surface hydrodynamic model.

[0078] S540: calling the precipitation-runoff simulation model and the two-dimensional surface water dynamics model in sequence within the same time interval, and obtaining the exchange flow of the precipitation-runoff simulation model and the two-dimensional surface water dynamics model through flow exchange calculation.

[0079] The exchange flow is the exchange flow data obtained by coupling the precipitation-runoff simulation model and the two-dimensional surface hydrodynamic model.

[0080] In some embodiments, the precipitation-runoff simulation model and the two-dimensional surface hydrodynamic model may be a SWMM model and a LISFLOOD-FP model, respectively.

[0081] In some embodiments, the processor may call the precipitation-runoff simulation model and the two-dimensional surface hydrodynamic model in sequence at the same time interval, use Python and related libraries to couple the precipitation-runoff simulation model and the two-dimensional surface hydrodynamic model, perform flow exchange calculations, and obtain exchange flow.

[0082] S550: Superimpose the exchange flow and the inflow flow of the node to obtain an updated inflow flow of the node.

[0083] S560: looping from S520 to S550 within a preset time to obtain the urban flood prediction result finally output by the two-dimensional surface hydrodynamic model, thereby completing a high-precision prediction of urban floods.

[0084] In some embodiments of the present specification, the processor uses the acquired rainwater pipe network data, building outline data, land use type data, digital elevation model and meteorological data to construct a precipitation-runoff simulation model and a two-dimensional surface water dynamic model to obtain urban flood prediction results. In this way, urban flood simulation with uneven rainfall distribution in time and space can be carried out; by using more detailed model input data, the precision of urban flood simulation is improved, and the simulation mechanism is more in line with the actual urban flood occurrence process; using Python and related libraries to couple the precipitation-runoff simulation model with the two-dimensional surface water dynamic model greatly simplifies the model coupling process.

Claims

1. A high-precision urban flood prediction method taking into account the uneven spatial and temporal distribution of rainfall, characterized in that: include: S1: Obtain the rainwater pipe network data, building outline data, land use type data and meteorological data of the target area respectively; S2: constructing a precipitation-runoff simulation model based on the rainwater pipe network data and the building outline data; S3: Based on the meteorological data, obtaining time series data and rainfall data by analyzing time resolution and time span; S4: constructing input data of a two-dimensional surface hydrodynamic model based on the building outline data, the rainfall data and the land use type data; S5: Input the time series data into the precipitation-runoff simulation model, perform flow exchange calculation with the two-dimensional surface hydrodynamic model based on the boundary conditions of the two-dimensional surface hydrodynamic model, obtain the urban flood prediction result, and complete the high-precision prediction of urban flood; wherein, S5 includes: S510: Inputting the time series data into the precipitation-runoff simulation model to obtain the coordinates of the rainwater inlet nodes; S520: Based on the node coordinates of the rainwater inlet, obtaining the inflow of the node; S530: establishing boundary conditions of a two-dimensional surface hydrodynamic model based on the inflow of the node; S540: calling the precipitation-runoff simulation model and the two-dimensional surface water dynamics model in sequence within the same time interval, and obtaining the exchange flow of the precipitation-runoff simulation model and the two-dimensional surface water dynamics model through flow exchange calculation; S550: superimposing the exchange flow and the inflow flow of the node to obtain an updated inflow flow of the node; S560: looping from S520 to S550 within a preset time to obtain the urban flood prediction result finally output by the two-dimensional surface hydrodynamic model, thereby completing a high-precision prediction of urban floods.

2. The high-precision urban flood prediction method according to claim 1, characterized in that: The S1 includes: Obtain stormwater network data, building outline data, land use type data and digital elevation model of the target area; The meteorological data is obtained by analyzing the monitoring data of the meteorological department and using the digital elevation model; wherein the meteorological data includes a three-dimensional array of rainfall.

3. The high-precision urban flood prediction method according to claim 1, characterized in that: The S2 includes: Generate building surface data based on the building outline data; A precipitation-runoff simulation model is constructed using stormwater pipe network data and building surface data.

4. The high-precision urban flood prediction method according to any one of claims 1 to 3, characterized in that: The S3 includes: Convert the building surface data into a new format to obtain building surface raster data; Based on the three-dimensional rainfall data and the building surface grid data, time resolution and time span analysis is performed to obtain the time series data and the rainfall data.

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