Radar and ground rainfall fused hydrodynamic urban flooding risk early warning method

Abstract

The invention discloses a radar and ground rainfall fused hydrodynamic urban flooding risk early warning method. The method comprises the steps of integrating rainfall station monitoring data, collecting radar echo map data and integrating rainfall forecasting grids; rainfall fusion interpolation is carried out based on actually measured rainfall of a rainfall station and a radar echo map; extracting a real-time rainfall grid sequence; extracting a forecast rainfall grid sequence; calculating surface net rain after rainfall deduction evaporation and soil absorption before coupling rainfall input of the two-dimensional rainfall boundary; constructing a two-dimensional rainfall boundary data file and a one-dimensional river channel upstream boundary data file of the HEC-RAS model; according to HEC-RAS model input and control parameters, the model is driven to calculate the maximum water depth and flooding duration; performing two-dimensional flooding risk assessment according to the flooding risk grade index; performing riverway flood risk assessment according to the riverway design flood and the flood control characteristic information; the problems of simulation and early warning application of the urban'rainstorm-waterlogging 'risk are solved.

Description

Technical Field

[0001] The invention belongs to the technical field of flood forecasting, and in particular relates to a hydrodynamic urban flooding risk early warning method that integrates radar and ground rainfall. Background Art

[0002] As the built-up area of ​​cities expands, the spatial distribution of rainstorms in urban areas is uneven, and the number of ground rainfall monitoring stations in urban areas is limited, which cannot accurately reflect the spatial distribution of rainstorms. Real-time weather radar echo maps can reflect the intensity, location, and movement changes of rainstorm disaster weather, but cannot reflect the intensity and amount of rainstorms. How to scientifically and accurately depict the uneven spatial distribution of rainfall and rainstorm intensity in urban areas during rainstorms has become the key to scientifically, accurately, and finely warning urban waterlogging. Integrating the measured rainfall at ground rainfall monitoring stations with weather radar echoes to accurately depict the spatial distribution of rainstorms has become an urgent need to carry out fine urban rainstorm waterlogging warnings and support urban flood control.

[0003] Usually, urban waterlogging simulation calculations are numerically simulated by coupling one- and two-dimensional hydrodynamics and urban drainage network models. For the HEC-RAS model, the coupled calculation of the drainage network is not supported. When the city has no drainage network data, the rainfall boundary conditions can be constructed based on the constructed one- and two-dimensional urban hydrodynamic model to simulate the urban "rainstorm-waterlogging" process. The infiltration calculation of HEC-RAS is difficult to combine with the commonly used local net rain empirical estimation method, and it is not convenient to convert the local empirical method into the infiltration calculation parameters or configuration of HEC-RAS, resulting in insufficient application flexibility, which is not conducive to the use of HEC-RAS for urban "rainstorm-waterlogging" risk simulation and early warning applications. Summary of the invention

[0004] The technical problem to be solved by the present invention is: to provide a hydrodynamic urban flooding risk warning method that integrates radar and ground rainfall, optimizes the spatial and temporal distribution of rainfall by fusing measured rainfall with radar echo rainfall, and uses an empirical method algorithm to quickly construct a net rain data set with spatial variation, so as to solve the technical problems that the infiltration calculation of HEC-RAS itself is difficult to combine with the commonly used local net rain empirical estimation method, and it is inconvenient to convert the local empirical method into the infiltration calculation parameters or configuration of HEC-RAS, resulting in insufficient application flexibility, which is not conducive to the use of HEC-RAS for urban "rainstorm-waterlogging" risk simulation and early warning application.

[0005] The technical solution of the present invention is:

[0006] A method for early warning of hydrodynamic urban flooding risk by integrating radar and ground rainfall, the method comprising:

[0007] Step 1: Integrate the monitoring data of rainfall stations, radar echo map data collection and forecast rainfall grid integration;

[0008] Step 2: Perform rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map;

[0009] Step 3, extracting a real-time rainfall grid sequence from the real-time fused rainfall grid;

[0010] Step 4, extracting a forecast rainfall grid sequence from the forecast rainfall grid;

[0011] Step 5: Before the two-dimensional rainfall boundary is coupled with the rainfall input, the net rainfall on the surface after deducting evaporation and soil absorption is obtained;

[0012] Step 6, construct the two-dimensional rainfall boundary data file of the HEC-RAS model;

[0013] Step 7: According to the upstream flood input scheme of the river channel, construct the one-dimensional upstream boundary data file of the HEC-RAS model;

[0014] Step 8: According to the HEC-RAS model input and control parameters, the model is driven to calculate the maximum water depth and flooding duration;

[0015] Step 9: Conduct a two-dimensional flooding risk assessment based on the flooding risk level index;

[0016] Step 10: Conduct river flood risk assessment based on river design flood and flood control characteristic information.

[0017] The integrated rain gauge monitoring data is parsed to obtain minute-level rainfall data so that it can be adapted and integrated with the 6-minute-level radar echo data;

[0018] Radar echo data acquisition stores the identified dBZ data as fixed-structure radar echo raster data under the premise of vector registration;

[0019] The forecast rainfall grid integration downloads the latest forecast rainfall grid to the local server in real time for temporary caching.

[0020] The rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map is to obtain the rainfall grid for the period through the radar echo map, the real-time data of the rain gauge station, the rain gauge station layer and the virtual station layer; the implementation method includes:

[0021] The radar echo image is parsed into a radar echo data grid by color adaptation;

[0022] The dBZ values ​​were sampled at each rainfall station, and the regression equation was used to fit the relationship between the radar echo intensity and the measured rainfall value during this period.

[0023] The dBZ value is sampled at the virtual station location, and the rainfall at the virtual station location is interpolated using the relationship between the radar echo intensity and the measured rainfall during the period;

[0024] Assume that the rainfall in the period is P i,t The sample set is

[0025] A={P 1, t,…,P n-1,t ,P n,t}

[0026] The mean value of radar echo in the time period R i,t-Δt The sample set is

[0027] B={R 1,t-Δt ,…,P n-1,t-Δt ,P n,t-Δt}

[0028] The rainfall during this period

[0029] P t =f A,B (R t-Δt )

[0030] Among them, f A,B is the regression fitting function of the rainfall and radar echo during the period; R t-Δt Indicates the rainfall during the period P t The dBZ mean value of the period Δt forward;

[0031] According to the above relationship, the mean dbZ value of the period corresponding to the location of the virtual station is converted into the rainfall amount of the period;

[0032] Finally, a two-dimensional interpolated rainfall grid is generated based on the measured rainfall and the interpolated rainfall of the virtual station.

[0033] The method for extracting the real-time rainfall grid sequence includes: obtaining a real-time rainfall grid sequence set through a real-time rainfall grid data source, a start time and an end time.

[0034] The calculation of surface net rainfall includes:

[0035] On the basis of the two-dimensional grid, the average stable infiltration rate Fc of different underlying surfaces is divided according to the urban area;

[0036] On the basis of two-dimensional grid, the net rainfall R of the period P in the grid unit is estimated by generalization according to the average stable infiltration rate Fc of the underlying surface:

[0037] When the rainfall P is greater than Fc,

[0038] R=P-Fc

[0039] When rainfall P <= Fc,

[0040] R=0.

[0041] The method for two-dimensional flooding risk assessment includes: deriving the HEC-RAS model to calculate the two-dimensional water depth time series results, traversing all grid cells with water depth greater than or equal to 15cm and less than 27cm, and water depth greater than or equal to 27cm and less than 40cm in the two-dimensional grid and accumulating the duration of flooding; then judging the risk level corresponding to the grid cells respectively according to the flooding risk level index; finally, outputting a two-dimensional risk level distribution grid; the flooding risk level index includes: when the maximum water depth of the grid cell does not exceed 15cm, the cell has no risk; when the maximum water depth of the grid cell is greater than 40cm, the cell has no risk. When the maximum water depth of the grid unit is greater than or equal to 15cm and less than 27cm, if the flooding time exceeds 30 minutes, the risk level of the unit is Level 4 Blue Warning, otherwise there is no risk; when the maximum water depth of the grid unit is greater than or equal to 27cm and less than 40cm, if the flooding time exceeds 15 minutes, the risk level of the unit is Level 3 Yellow Warning, otherwise it is Level 4 Blue Warning; when the maximum water depth of the grid unit is greater than or equal to 40cm and less than 60cm, the risk level of the unit is Level 2 Orange Warning; when the maximum minimum depth of the grid unit is greater than or equal to 60cm, the risk level of the unit is Level 1 Red Warning.

[0042] The method for river flood risk assessment based on river design flood and flood control characteristic information includes: judging whether the water level in the calculation result of the one-dimensional river has reached the corresponding risk level indicator based on the flow and water level corresponding to the design standard provided by each river section, as well as the warning water level and guaranteed water level information; finally, deriving all river sections with flood risks, the corresponding flood risk levels, the occurrence time and the triggering event information.

[0043] The beneficial effects of the present invention are:

[0044] The present invention integrates and drives the model based on the urban HEC-RAS one- and two-dimensional hydrodynamic model, optimizes the spatial distribution of rainfall through radar echo rainfall fusion, and realizes the rapid estimation of the net rainfall of spatial rainfall through empirical analysis methods under the premise of considering regional differences. It is combined with the one- and two-dimensional hydrodynamic model to realize the application and practice of urban flooding risk warning.

[0045] The present invention optimizes the spatiotemporal distribution of rainfall by fusing measured rainfall with radar echoes, and uses an empirical method algorithm to quickly construct a net rainfall dataset with spatial variations. It solves the problem of combining the HEC-RAS model with the autonomous net rainfall empirical estimation method, and simulating and warning the risk of "rainstorm-waterlogging" in cities.

[0046] It solves the problem that the infiltration calculation of HEC-RAS is difficult to combine with the commonly used local empirical estimation method of net rain, and it is inconvenient to convert the local empirical method into the infiltration calculation parameters or configuration of HEC-RAS, resulting in insufficient application flexibility and being unfavorable for the use of HEC-RAS for simulation and early warning of urban "rainstorm-waterlogging" risks. DETAILED DESCRIPTION

[0047] A hydrodynamic urban flooding risk warning method based on radar and ground rainfall fusion is generally divided into two parts: preprocessing and flooding simulation analysis. Preprocessing is divided into: perception data integration, real-time rainfall grid fusion calculation, real-time rainfall grid extraction and forecast rainfall grid extraction. Flooding simulation analysis is divided into: net rain grid calculation, two-dimensional rainfall boundary construction, flood boundary data construction, driving HEC-RAS model, two-dimensional flooding risk assessment and river flood risk assessment.

[0048] Step 1: Perception data integration: integrating rainfall station monitoring data, radar echo map data collection, and forecast rainfall grid integration.

[0049] This step mainly integrates rainfall station monitoring data, radar echo map data collection, and forecast rainfall grid integration. The data sources are as follows:

[0050] Rainfall station monitoring data: obtain rainfall monitoring data from the real-time water and rainfall database.

[0051] Radar echo map: Get the radar echo map of the model area from the meteorological department.

[0052] Prediction rainfall grid: Obtain the regional short-term forecast rainfall grid from satellite radar rainfall measuring equipment or meteorological departments.

[0053] To ensure the timeliness of data integration, the corresponding data integration program should refresh data from the data source every 1-2 minutes to obtain the latest data.

[0054] The rain gauge integration program needs to parse the minute-level rainfall data in a timely manner through short-time refresh so as to adapt and integrate it with the 6-minute radar echo data.

[0055] The radar echo map acquisition program needs to store the identified dBZ data as fixed-structure radar echo raster data under the premise of vector registration.

[0056] The main function of the rainfall forecast grid acquisition program is to download the latest rainfall forecast grid to the local server for temporary caching in real time, so as to quickly respond to the model data input requirements.

[0057] Step 2: Real-time rainfall grid fusion: perform rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map.

[0058] This step is to perform rainfall fusion interpolation based on the measured rainfall at the rain gauge and the radar echo map. Fusion of radar echo data can partially improve the spatial distribution of rainfall after two-dimensional interpolation, making the rainfall area more realistic.

[0059] The rainfall grid for each period is obtained through radar echo map, real-time data of rain gauge station, rain gauge station layer and virtual station layer.

[0060] Specifically include:

[0061] Radar echo recognition: If only radar echo image data is available, the recognition program can be used to parse the radar echo image into radar echo data grids by color adaptation.

[0062] Note:

[0063] 1. Remove clutter with low dBZ value, such as considering dBZ>15 as effective value.

[0064] 2. The problem of geo-registration of radar echo maps.

[0065] Radar echo sampling and fitting during the period: dBZ values ​​are sampled at multiple rain gauge locations, and the regression equation is used to fit the relationship between the radar echo intensity and the measured rainfall value during the period.

[0066] Note:

[0067] 1. The rainfall data of the rain gauge station should be refreshed from the rainfall standard database in real time to ensure that the rainfall changes within the time period can be obtained in time, so as to parse the rainfall data at the minute level.

[0068] 2. The measured data at the rain gauge station may be earlier than the radar echo data. When matching the radar echo data and the measured rainfall data, necessary time misalignment correction should be made.

[0069] 3. When sampling the radar echo data corresponding to rainfall in a minute period, the period average of dBZ corresponding to the start and end time points of the rainfall period should be used.

[0070] 4. Longer dbZ sampling should be considered to improve the rationality of the fitting relationship.

[0071] Virtual station sampling: Use the virtual station location to sample dBZ values, and use the relationship between the radar echo intensity and the measured rainfall to interpolate the rainfall at the virtual station location. The layout of virtual stations should be based on the density distribution of actual rainfall monitoring stations, the area of ​​urban built-up areas, and the characteristics of urban rainstorms. The distance between stations should not be greater than 3km. When the area of ​​urban built-up areas is small, it should not be greater than 1 km.

[0072] Assume that the rainfall in the period is P i,t The sample set is

[0073] A={P 1,t ,…,P n-1,t ,P n,t}

[0074] The mean value of radar echo in the time period R i,t-Δt The sample set is

[0075] B={R 1,t-Δt ,…,P n-1,t-Δt ,P n,t-Δt}

[0076] The rainfall during this period

[0077] P t =f A,B (R t-Δt )

[0078] Among them, f A,B is the regression fitting function of the rainfall and radar echo during the period; R t-Δt Indicates the rainfall during the period P t The dBZ mean value for the period Δt forward.

[0079] According to the above relationship, the dbZ mean value of the period corresponding to the virtual station can be converted into the rainfall amount of the period.

[0080] Two-dimensional rainfall integrated interpolation calculation: Generate a two-dimensional interpolated rainfall grid based on the measured rainfall and the interpolated rainfall of the virtual station.

[0081] Taking into account the time it takes for rain to fall from clouds to the ground, rainfall data and radar echo data should be appropriately time-shifted and matched, preferably about 3-5 minutes.

[0082] Step 3: Real-time rainfall grid extraction: extract the real-time rainfall grid sequence from the real-time fused rainfall grid results.

[0083] This step is to extract the real-time rainfall grid sequence from the real-time fused rainfall grid results.

[0084] The real-time rainfall grid sequence set is obtained through the real-time rainfall grid data source, start time and end time.

[0085] Specifically include:

[0086] Rainfall extraction: Extract hourly rainfall grid sequences according to start and end time.

[0087] Step 4: Extract forecast rainfall grid: extract forecast rainfall grid sequence from forecast rainfall data source.

[0088] This step is to extract the forecast rainfall grid sequence from the forecast rainfall data source.

[0089] The forecast rainfall grid sequence set is obtained through the forecast rainfall grid data source.

[0090] Specifically include:

[0091] Extract the future rainfall grid data. The number of time periods is determined according to the actual forecast grid data.

[0092] Step 5, net rain grid calculation: Before the two-dimensional rainfall boundary coupling rainfall input, it is necessary to obtain the surface net rain after deducting evaporation and soil absorption from rainfall. In the present invention, the net rain calculation is generalized by deducting the stable infiltration of the underlying surface from rainfall.

[0093] 1) On the basis of two-dimensional grids, the average stable infiltration rate Fc (unit: mm / h) of different underlying surfaces is divided according to the urban area.

[0094] 2) Based on the two-dimensional grid, the average stable infiltration rate Fc of the urban area is used to estimate the period rainfall P (unit: mm / h) and the period net rainfall R (unit: mm / h) in the grid unit. That is:

[0095] When the rainfall P is greater than Fc,

[0096] R=P-Fc

[0097] When rainfall P <= Fc,

[0098] R=0

[0099] Step 6: 2D rainfall boundary construction: construct the 2D rainfall boundary data file of the HEC-RAS model, that is, pack and store the 2D time series rainfall grid data into the rainfall boundary data file.

[0100] This step is to construct the two-dimensional rainfall boundary data file (.dss file) of the HEC-RAS model through the program based on the net rain grid sequence. DSS files can store two-dimensional grid data with time series, and can be easily generated through programming languages ​​such as Python and related program libraries.

[0101] Step 7: Construction of flood boundary data: According to the upstream flood input scheme of the river channel, the flood inflow time series data of the upstream river channel in the model calculation area are packaged to construct the one-dimensional upstream boundary data file of the HEC-RAS model.

[0102] This step is to construct a one-dimensional upstream river boundary data file of the HEC-RAS model based on the upstream flood input scheme. The upstream flood data can be derived from the forecast results of the existing hydrological forecasting system, or it can be manually input or selected from a flow process line data.

[0103] Step 8, drive the HEC-RAS model: use the two-dimensional rainfall boundary data file and the one-dimensional river upstream boundary data file as the input of the HEC-RAS model, set the control parameters such as calculation step size and duration, and use the model encapsulation interface to drive the model to calculate the two-dimensional water depth time series results, including water depth and flooding duration.

[0104] This step is to drive the HEC-RAS model calculation based on the HEC-RAS model input and control parameters. The Python program can be used to encapsulate the model that drives HEC-RAS. Before calculation, the data changes of the boundary data file and the setting of the model initial conditions must be completed correctly.

[0105] Step 9: Two-dimensional flooding risk assessment: Based on the output results of maximum water depth and flooding duration, a two-dimensional flooding risk assessment is conducted according to the flooding risk level index.

[0106] This step is to conduct a two-dimensional flooding risk assessment based on the output results of the maximum water depth and flooding duration and the flooding risk level index.

[0107] First, it is necessary to export the HEC-RAS model to calculate the two-dimensional water depth time series results, traverse all grid cells with a water depth greater than or equal to 15 cm and less than 27 cm, and a water depth greater than or equal to 27 cm and less than 40 cm in the two-dimensional grid, and accumulate the duration of flooding; then, determine the risk level corresponding to the grid cell according to the flooding risk level index; finally, output it as a two-dimensional risk level distribution grid.

[0108] According to the definition of flooding risk level index, when the maximum water depth of a grid unit does not exceed 15cm, the unit is risk-free; when the maximum water depth of a grid unit is greater than or equal to 15cm and less than 27cm, if the flooding time exceeds 30 minutes, the unit's risk level is level 4 (blue warning), otherwise there is no risk; when the maximum water depth of a grid unit is greater than or equal to 27cm and less than 40cm, if the flooding time exceeds 15 minutes, the unit's risk level is level 3 (yellow warning), otherwise it is level 4 (blue warning); when the maximum water depth of a grid unit is greater than or equal to 40cm and less than 60cm, the unit's risk level is 2 (orange warning); when the maximum minimum depth of a grid unit is greater than or equal to 60cm, the unit's risk level is level 1 (red warning). The above flooding risk level indicators can be adjusted as necessary according to the actual regional conditions.

[0109] Step 10: River flood risk assessment: Based on the calculation results of the water level and flow of the river section, the river flood risk assessment is carried out according to the river design flood and flood control characteristics information.

[0110] This step is to conduct a river overflow and waterlogging risk assessment based on the results of river section water level and flow calculations and the river design flood and flood control characteristic information.

[0111] According to the flow (water level) corresponding to the design standard provided by each river section, as well as the warning water level and guaranteed water level information, it is judged whether the water level in the calculation result of the one-dimensional river has reached the corresponding risk level indicator. Finally, all river sections with flood risks, corresponding flood risk levels, occurrence time and triggering events are derived.

[0112] The river flood risk level indicators are defined as follows: when the water level overflows the embankment, it is Level 1 (red warning); when the water level exceeds the guaranteed water level and is lower than the embankment top, it is Level 2 (orange warning); when the water level exceeds the warning water level and is lower than the guaranteed water level, it is Level 3 (yellow warning); when the water level is within 0.5 meters below the warning water level, it is Level 4 (blue warning).

[0113] The basic data of the present invention include:

[0114] (1) Administrative division layer data. It is mainly used to divide the warning area and bind the area of ​​disaster protection objects, so as to facilitate the identification and statistics of warnings based on administrative regions.

[0115] (2) River basin data. Mainly includes rivers (water systems) in and around the city, river control section layers and supporting information data.

[0116] (3) Station data. Mainly includes river water level stations and rainfall stations in the urban area and surrounding areas and supporting information data.

[0117] (4) Data on disaster-affected protected areas. Mainly including flood-prone areas, urban roads, bus stops, underground parking lots, underground passages, government agency locations, schools, hospitals and other important areas, facilities and units.

[0118] (5) Model data: mainly including the structural data of the model and the results data of the model calculation.

[0119] (6) Perception data. Mainly includes rainfall monitoring data, river water condition monitoring data, radar echo data, and video AI monitoring data.

[0120] (7) Forecast data. Mainly includes satellite rainfall forecast grid data and river basin hydrological forecast data.

[0121] Step 12: Information display includes:

[0122] (1) Basic information base map display. The GIS base map is used to display the rainfall stations, flood-prone areas and major river systems in the urban area and its surrounding areas, and is used as a framework container for the application of the entire urban flood risk warning model, expanding and implementing the loading and display of other functional modules. The GIS map framework provides basic GIS object query and interaction functions, such as the ability to click to browse relevant information and data of the rainfall station.

[0123] (2) Information browsing and display of integrated data. The real-time rainfall grid and radar echo map can be superimposed on the map, and can be displayed in time series through the timeline; the real-time monitoring data and time series process data of rainfall stations and water level stations can be displayed; the video AI monitoring data (such as flood depth) and real-time video link display can be realized.

[0124] (3) Flood risk warning model drive. The main urban area flood risk warning model is manually started. Each calculation requires the creation of a corresponding model calculation task and the configuration of the model's rainfall input. The rainfall data can be real-time and forecast rainfall, or artificially simulated rainfall data. The flood data can be basin hydrological forecast data, or designed flood data or artificially simulated data. The boundary condition can be the water level or flow of a one-dimensional river channel. It can also support the setting of some model control parameters. After confirming the start of the model, the model calculation status needs to be displayed and the completion status needs to be prompted in a timely manner. Due to limited computing resources, it is necessary to control that only one batch calculation can be started at a time, and it can be terminated manually.

[0125] (4) Flooding risk assessment display. The two-dimensional flooding time series dynamic effect and risk area rendering results calculated by the batch model are superimposed on the GIS base map. The water depth and flooding duration information of the flooded area can be queried, and early warning prompts can be issued for flood-prone points based on the flooding situation. The river section with flood risk is displayed, and relevant flood risk information can be browsed.

[0126] (5) Display of flooding risk association. This is mainly based on the map display of disaster-prone objects associated with flooding risk. When displaying disaster-prone objects with flooding risk, it is necessary to provide risk level prompts. The model supports comparing the flooding characteristic water level of the disaster-prone object with the flooding water level simulated by the model to describe the flooding risk of the disaster-prone object.

[0127] (6) Warning release. The main content of warning release is to generate warning briefs. The warning briefs will be designed with relatively fixed templates, mainly describing the area of ​​the flooded area, the corresponding area of ​​each risk level, the flooding situation of each administrative area, and the flooding risk situation of the disaster-bearing protection objects. Warning release personnel can edit and confirm the generated warning briefs. Warning briefs can be used as reference materials for reporting to higher-level authorities or leaders. Warning briefs can be exported as documents or sent to other application systems through the integrated third-party warning release interface.

Claims

1. A hydrodynamic urban flooding risk early warning method integrating radar and ground rainfall, characterized by: The method comprises: Step 1: Integrate the monitoring data of rainfall stations, radar echo map data collection and forecast rainfall grid integration; Step 2: Perform rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map; Step 3, extracting a real-time rainfall grid sequence from the real-time fused rainfall grid; Step 4, extracting a forecast rainfall grid sequence from the forecast rainfall grid; Step 5: Before the two-dimensional rainfall boundary is coupled to the rainfall input, the net rainfall on the surface after deducting evaporation and soil absorption is obtained; Step 6, construct the two-dimensional rainfall boundary data file of the HEC-RAS model; Step 7: According to the upstream flood input scheme of the river channel, construct the one-dimensional upstream boundary data file of the HEC-RAS model; Step 8: According to the HEC-RAS model input and control parameters, the model is driven to calculate the maximum water depth and flooding duration; Step 9: Conduct a two-dimensional flooding risk assessment based on the flooding risk level index; Step 10: Conduct river flood risk assessment based on river design flood and flood control characteristic information.

2. According to claim 1, a radar and ground rainfall fusion hydrodynamic urban flooding risk early warning method is characterized by: The integrated rain gauge monitoring data is parsed to obtain minute-level rainfall data so as to be adapted and integrated with the 6-minute-level radar echo data; Radar echo data acquisition stores the identified dBZ data as fixed-structure radar echo raster data under the premise of vector registration; The forecast rainfall grid integration downloads the latest forecast rainfall grid to the local server in real time for temporary caching.

3. The method for early warning of hydrodynamic urban flooding risk by integrating radar and ground rainfall according to claim 1 is characterized by: The rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map is to obtain the rainfall grid for the period through the radar echo map, the real-time data of the rain gauge station, the rain gauge station layer and the virtual station layer; the implementation method includes: The radar echo image is parsed into a radar echo data grid by color adaptation; The dBZ values ​​were sampled at each rainfall station, and the regression equation was used to fit the relationship between the radar echo intensity and the measured rainfall value during this period. The dBZ value is sampled at the virtual station location, and the rainfall at the virtual station location is interpolated using the relationship between the radar echo intensity and the measured rainfall during the period; Assume that the rainfall in the period is P i，t The sample set is A={P 1，t ,…,P n-1，t ,P n，t } The mean value of radar echo in the time period R i,t-Δt The sample set is B={R 1，t-Δt ,…,P n-1，t-Δt ,P n，t-Δt } The rainfall during this period P t =f A，B (R t-Δt ) Among them, f A，B is the regression fitting function of the rainfall and radar echo during the period; R t-Δt Indicates the rainfall during the period P t The mean dBZ value of the period Δt forward; According to the above relationship, the mean dbZ value of the period corresponding to the location of the virtual station is converted into the rainfall amount of the period; Finally, a two-dimensional interpolated rainfall grid is generated based on the measured rainfall and the interpolated rainfall of the virtual station.

4. The method for early warning of hydrodynamic urban flooding risk by integrating radar and ground rainfall according to claim 1 is characterized by: The method for extracting the real-time rainfall grid sequence includes: obtaining a real-time rainfall grid sequence set through a real-time rainfall grid data source, a start time and an end time.

5. The method for early warning of hydrodynamic urban flooding risk by integrating radar and ground rainfall according to claim 1 is characterized by: The calculation of surface net rainfall includes: On the basis of the two-dimensional grid, the average stable infiltration rate Fc of different underlying surfaces is divided according to the urban area; On the basis of two-dimensional grid, the net rainfall R of the period P in the grid unit is estimated by generalization according to the average stable infiltration rate Fc of the underlying surface: When the rainfall P is greater than Fc, R=P-Fc When rainfall P <= Fc, R＝0。 6. The method for early warning of hydrodynamic urban flooding risk by integrating radar and ground rainfall according to claim 1 is characterized by: The method for two-dimensional flooding risk assessment includes: deriving the HEC-RAS model to calculate the two-dimensional water depth time series results, traversing all grid cells with water depth greater than or equal to 15cm and less than 27cm, and water depth greater than or equal to 27cm and less than 40cm in the two-dimensional grid and accumulating the duration of flooding; then judging the risk level corresponding to the grid cells respectively according to the flooding risk level index; finally, outputting a two-dimensional risk level distribution grid; the flooding risk level index includes: when the maximum water depth of the grid cell does not exceed 15cm, the cell has no risk; when the maximum water depth of the grid cell is greater than 40cm, the cell has no risk. When the maximum water depth of the grid unit is greater than or equal to 15cm and less than 27cm, if the flooding time exceeds 30 minutes, the risk level of the unit is Level 4 Blue Warning, otherwise there is no risk; when the maximum water depth of the grid unit is greater than or equal to 27cm and less than 40cm, if the flooding time exceeds 15 minutes, the risk level of the unit is Level 3 Yellow Warning, otherwise it is Level 4 Blue Warning; when the maximum water depth of the grid unit is greater than or equal to 40cm and less than 60cm, the risk level of the unit is Level 2 Orange Warning; when the maximum minimum depth of the grid unit is greater than or equal to 60cm, the risk level of the unit is Level 1 Red Warning.

7. The method for early warning of hydrodynamic urban flooding risk by integrating radar and ground rainfall according to claim 1 is characterized by: The method for river flood risk assessment based on river design flood and flood control characteristic information includes: judging whether the water level in the calculation result of the one-dimensional river has reached the corresponding risk level indicator based on the flow and water level corresponding to the design standard provided by each river section, as well as the warning water level and guaranteed water level information; finally, deriving all river sections with flood risks, the corresponding flood risk levels, the occurrence time and the triggering event information.