Urban waterlogging risk early warning method based on radar and ground rainfall fusion

Abstract

The invention discloses an urban water flooding risk early warning method based on radar and ground precipitation fusion. The method comprises the steps of terrain analysis and calculation; analyzing a waterlogging area and flooding grid filling, and generating a flooding area watershed topology; rainfall station monitoring data and radar echo map data are integrated, and rainfall forecast grids are integrated; rainfall fusion interpolation is carried out based on actually measured rainfall of a rainfall station and a radar echo map; matching a rainfall grid; extracting an average rainfall sequence of all flooding basin surfaces; extracting an average rainfall sequence of the sub-basin units from the forecast rainfall source; loading and analyzing an input file, constructing an internal data structure and creating a result output grid template; calculating rainfall flooding; performing flooding risk assessment and early warning display; the problems that in the prior art, data information needed in urban water accumulation risk early warning analysis is numerous, and particularly, the accurate acquisition difficulty of urban drainage pipe network data is large are solved; the problem that urban rainstorm intensity and rainstorm volume space distribution cannot be accurately reflected in the prior art is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of flood warning and relates to a method for early warning of urban flooding risk based on the fusion of radar and ground precipitation. Background Art

[0002] The simulation analysis of urban rainstorm waterlogging generally uses one-dimensional and two-dimensional hydrodynamic models coupled with urban drainage network models for numerical simulation. However, this method requires a large amount of data, especially data on urban drainage networks, which is extremely challenging to obtain accurately. At the same time, the calculation process of this simulation analysis has high requirements for computing performance and resources, resulting in increased operating costs. In the face of insufficient drainage network resources, a method for quickly estimating urban rainstorm waterlogging has become an urgent need to support urban flood control work.

[0003] In addition, with the expansion of urban built-up areas, the spatial distribution of urban rainstorms is becoming increasingly uneven. Due to the limited number of ground rainfall monitoring stations in urban areas, they cannot accurately reflect the spatial distribution of rainstorms. Although real-time weather radar echo maps can show the intensity, location and movement changes of rainstorms, they cannot provide the specific intensity and precipitation of rainstorms. Therefore, scientifically and accurately depicting the uneven distribution of rainfall and rainstorm intensity in urban space during a single rainstorm has become the key to accurately warning urban waterlogging. By integrating the measured rainfall at ground rainfall monitoring stations with weather radar echo data, accurately depicting the spatial distribution of rainstorms is particularly important and urgent for refining urban rainstorm waterlogging warnings and supporting urban flood control work. Summary of the invention

[0004] The present invention aims to solve the following technical problems: provide a method for early warning of urban flooding risk based on the fusion of radar and ground precipitation, so as to overcome the numerous data and information required in the existing technology for early warning analysis of urban flooding risk, especially the difficulty in accurately obtaining urban drainage network data; overcome the problems that the existing technology cannot accurately reflect the spatial distribution of urban rainstorm intensity and rainstorm amount. At the same time, the present invention aims to reduce the demand for high computing performance and resources for simulation analysis and calculation, thereby reducing operating costs and improving the economy and practicality of early warning technology.

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

[0006] A method for early warning of urban flooding risk based on the fusion of radar and ground precipitation, the method comprising:

[0007] Step 1: terrain analysis and calculation;

[0008] Step 2: Based on the results of terrain analysis, conduct flood-prone area analysis and flooding grid filling analysis, and generate flooding area watershed topology;

[0009] Step 3: Integrate the monitoring data of rainfall stations, radar echo map data collection and forecast rainfall grid integration;

[0010] Step 4: Perform rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map;

[0011] Step 5: Rainfall grid adaptation;

[0012] Step 6, extracting the average rainfall series of all flooded watersheds;

[0013] Step 7, extracting the average rainfall sequence of the sub-basin unit from the forecast rainfall source;

[0014] Step 8: Load and parse the input file, build the internal data structure and create the output grid template;

[0015] Step 9: Calculation of rainfall accumulation;

[0016] Step 10: Flooding risk assessment and early warning display.

[0017] The terrain analysis calculation includes: river channel depiction, DEM clipping, low-lying land analysis, terrain filling analysis and terrain confluence analysis; the river channel depiction is based on the actual water system vector line as a template, and the water system is forced to be depicted on the DEM; the DEM clipping is to spatially clip the DEM with the water system surface layer; the water surface clipping area is regarded as a non-flooded area, as the end point of two-dimensional confluence, and no downstream waterlogging occurs; the low-lying land analysis performs terrain filling processing on the basis of the clipped DEM, and marks the lowest point of the filling area with an area greater than the threshold as a depression; the terrain filling analysis performs filling analysis and calculation based on the depicted and clipped DEM terrain; the terrain confluence analysis performs flow direction and flow analysis based on the terrain after filling, and uses 1-2km 2 Generates a catchment-based drainage layer for a benchmark threshold.

[0018] The method for analyzing the flood-prone area includes: marking the exit point of the depression based on the location of the depression and the DEM and flow direction grid after filling, and traversing the depression to generate the flood-prone area distribution layer; marking the outflow boundary point based on the flow direction grid; splitting the basic water system according to the exit point of the depression to form a confluence water system layer, and generating the supporting water system watershed grid and layer at the same time; constructing the topological relationship of the rainwater collection area from the depression exit to the outflow boundary point; generating the rainwater collection area grid based on the topological relationship of the rainwater collection area, and establishing the association relationship between the rainwater collection area and the internal water system watershed; the flooding grid filling analysis includes: for each rainwater collection area, starting from the depression, the gradient index is constructed from low to high for the grid cells in the domain according to a certain gradient, so as to improve the unit speed when doing flooding filling; for the rainwater collection area topological layer:

[0019] Mark the depression outlet and outflow boundary points and the upstream and downstream topological relationships;

[0020] The exit points of the depression and the boundary points of the two-dimensional outflow are regarded as flooded topological nodes, and the attribute values ​​of the flood-prone area are associated with them;

[0021] By generalizing the drainage capacity coefficient per unit area per unit time D k ;

[0022] D k =αP 设计频率

[0023] W v,t =D k ×T×A / 1000

[0024] W v,t T is the amount of water discharged from the flooded area A in time t, and A is the area of ​​the flooded area at time t.

[0025] The integrated rain gauge monitoring data is parsed to obtain minute-level rainfall data; radar echo map data collection stores the identified dBZ data as a fixed-structure radar echo grid 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.

[0026] The rainfall fusion interpolation method based on the measured rainfall at the rain gauge station and the radar echo map includes: obtaining the time period rainfall grid based on the radar echo map, the real-time data of the rain gauge station, the rain gauge station layer and the virtual station layer; parsing the radar echo image into the radar echo (dBZ) data grid by color adaptation using a recognition program; sampling the dBZ value at each rain gauge station location, using the regression equation to fit the relationship between the radar echo intensity and the measured rainfall value for this period, and converting the time period dbZ mean value of the corresponding position of the virtual station into the time period rainfall; generating a two-dimensional interpolation rainfall grid based on the measured rainfall and the virtual station rainfall calculated by regression.

[0027] The rainfall grid adaptation is to obtain a rainfall adaptation index dataset through rainfall grid structure information, watershed grid of the rain-collecting area and topological layer of the rain-collecting area. The rainfall adaptation index dataset is a rainfall grid index dataset of each watershed in the rain-collecting area. The rainfall grid adaptation method comprises: traversing the watershed of the basin unit contained in the rain-collecting area, counting the grid cells corresponding to the rainfall in each grid cell in the watershed, and forming rainfall grid cell index and weight value pair data; packaging and combining all rainfall adaptation data in the rain-collecting area into a rainfall adaptation index dataset.

[0028] The method for extracting the average rainfall sequence of all flooded watersheds includes: obtaining a rainfall sequence set of a rain-collecting area through a rainfall adaptation index data set and a real-time rainfall grid sequence; and extracting a rainfall sequence set for each rain-collecting area one by one according to the rainfall adaptation index data set.

[0029] The method for extracting the average rainfall sequence of sub-basin units from the forecast rainfall source includes: obtaining a rainfall sequence set for a rain-collecting area through a rainfall adaptation index data set and a forecast rainfall grid sequence, and extracting a rainfall sequence set for each rain-collecting area one by one according to the rainfall adaptation index data set.

[0030] The rainfall accumulation calculation includes:

[0031] Benchmark drainage loss deduction: Benchmark drainage loss deduction is performed on the rainfall data within each calculation step;

[0032] Calculation of water drainage and water withdrawal in flooded areas: Calculate the water withdrawal in flooded areas according to the area drainage coefficient;

[0033] Calculation of overflow water volume in flooded areas: When the flooded water volume exceeds the critical overflow water volume, the overflow calculation is started and the overflow water volume is transferred to the downstream flooded areas;

[0034] Convert the rainfall in the calculation step after deducting the loss of the benchmark drainage in the catchment area into water volume W L,t , superimposed with the original water volume W in this area t-1 , upstream overflow water volume W U,t , and deduct the drainage water volume W in this area v,t Then, the total water volume W in the flooded area within the current calculation step is obtained. t Right now:

[0035] W t =W L,t +W U,t +W 0,t-1 -W v,t

[0036] When the water level in the flooded area exceeds the dry-wet boundary of the outlet, that is, the flooded water volume exceeds the overflow critical water volume W c When the generalized overflow coefficient K w Deduct the water volume in this area and transfer this overflow water to the downstream flooded area

[0037] W t =W t -K w ·(W t -W c ).

[0038] The flooding risk assessment and early warning display method includes: assessing the flooding risk according to the flooding risk level index. According to the definition of the flooding risk level index, when the maximum water depth of the grid unit does not exceed 15cm, the unit has no risk; when the maximum water depth of the grid unit is greater than 15cm and less than 27cm, if the flooding time exceeds 30 minutes, the risk level of the unit is level 4, 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, otherwise it is level 4; 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 2; 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.

[0039] The beneficial effects of the present invention are:

[0040] The present invention is a method for constructing the topology of the watershed in the flood-prone area based on the terrain characteristics of the urban area, and quickly estimating the flooding situation in the flood-prone area based on the rainfall runoff in the watershed in the flood-prone area. Different from the calculation method of coupling the urban pipe network with hydrodynamics, the present invention requires less basic information, can more quickly analyze and estimate the flooding situation in the urban flood-prone area, and realize rapid early warning of urban flooding risks.

[0041] The present invention does not require accurate urban drainage network data; can conduct targeted rapid assessment and analysis of historical flood-prone points; can quickly implement risk analysis and calculation, and provide information support for urban waterlogging prevention and control in real time.

[0042] The present invention utilizes radar remote sensing technology and ground precipitation observation data and terrain combining technical means to improve the accuracy and timeliness of urban waterlogging warning.

[0043] The present invention optimizes the calculation of spatial distribution of rainfall in urban areas through radar echo rainfall fusion, so that the rainfall input is more consistent with the actual rainfall distribution, and solves the problem of large distortion of two-dimensional spatial interpolation of rainfall due to insufficient density of measured rainfall stations in urban areas, thereby supporting more precise urban rainstorm waterlogging warning.

[0044] The present invention solves the problem of the large amount of data and information required in the existing technology for early warning analysis of urban waterlogging risks, especially the difficulty in accurately obtaining urban drainage network data; it overcomes the problem that the existing technology cannot accurately reflect the spatial distribution of urban rainstorm intensity and rainstorm volume. At the same time, the present invention aims to reduce the demand for high computing performance and resources for simulation analysis and calculation, thereby reducing operating costs and improving the economy and practicality of early warning technology. DETAILED DESCRIPTION

[0045] The invention discloses an urban flooding risk early warning method based on the fusion of radar and ground precipitation, which comprises two parts: preprocessing and flooding simulation analysis.

[0046] Preprocessing includes terrain analysis, flood accumulation feature analysis, sensor data integration, rainfall fusion calculation, rainfall grid adaptation, real-time rainfall extraction and forecast rainfall extraction. Flood accumulation simulation analysis includes data initialization, rainfall flood accumulation calculation and flood accumulation risk assessment.

[0047] Step 1: Terrain analysis: Perform river channel delineation, water surface clipping, depression analysis, flow direction and flow analysis based on urban DEM data.

[0048] This step mainly conducts a series of terrain analysis calculations based on the unfilled DEM of the urban area (also known as: original DEM), real river (channel) vector lines and water system surfaces. The main operations include: river channel characterization, DEM clipping, low-lying land analysis, terrain filling analysis and terrain confluence analysis.

[0049] The river channel depiction is based on the actual water system vector line as a template, and the water system is forcibly depicted on the DEM. The purpose is to correct the problem that the generated confluence water system deviates from the actual water system due to DEM data errors and filling depressions.

[0050] DEM clipping mainly uses the water system surface layer to spatially clip the DEM. The model regards the water surface clipping area as a non-flooded area, as the end point of two-dimensional confluence, and does not cause downstream backwater.

[0051] Based on the above clipped DEM results, the low-lying area analysis is carried out by filling the terrain depressions and marking the areas larger than a certain threshold (e.g. 1600m 2 ) is the lowest point of the filled area above the surface.

[0052] The terrain filling analysis is based on the depicted and clipped DEM terrain for filling analysis calculation.

[0053] The terrain confluence analysis is based on the flow direction and flow analysis of the terrain after filling, and the flow rate is analyzed at a rate of 1-2 km. 2 Generates a catchment-based drainage layer for a benchmark threshold.

[0054] Step 2: Analysis of flooding characteristics: Based on the results of terrain analysis, conduct flood-prone area analysis and flooding grid filling analysis, and generate the flooding area watershed topology.

[0055] This step is to conduct flood-prone area analysis and flooding grid filling analysis based on the results of terrain analysis, and generate the flooding area watershed topology.

[0056] Through the filled depression DEM, depression layer, flow direction raster, flow raster and benchmark drainage capacity D b(Unit: mm / h) The partition grid obtains the flood-prone area distribution layer, the catchment area topology layer, the confluence water system layer, the catchment area watershed raster, the catchment area watershed layer and the flooding grid filling index dataset. Among them, the catchment area topology map reflects the upstream and downstream topological relationship between the depression outlet and the outflow boundary point; the flooding grid filling index dataset is used to improve the speed of unit flooding filling.

[0057] The flood prone area analysis includes:

[0058] Based on the location of the depression, the DEM after filling and the flow direction grid, the depression exit point is marked, and the flood-prone area distribution layer is generated by traversing the depression area;

[0059] The flow boundary points are marked based on the flow direction grid; the basic water system is split according to the depression exit points to form a confluence water system layer, and the supporting water system watershed grid and layer are generated at the same time.

[0060] Construct the topological relationship of the rainwater collection area from the depression outlet to the outflow boundary point.

[0061] The rainwater catchment area grid is generated based on the topological relationship of the rainwater catchment area, and the association between the rainwater catchment area and the internal water system watershed is established.

[0062] The flooding grid fill analysis includes:

[0063] For each rainwater collection area, a gradient index is established from low to high for the grid cells in the domain starting from the depression according to a certain gradient (such as 0.2 meters), so as to increase the cell speed when doing flood filling.

[0064] For the rainwater catchment area topology layer, include:

[0065] 1) It is necessary to mark the outlet and outflow boundary points of the depression and the upstream and downstream topological relationship, and expand: rainwater collection area, whether it is a flooded node, whether it is a flood-prone area, basic drainage flow, overflow coefficient, and water accumulation capacity attributes.

[0066] 2) The exit points of the depression and the two-dimensional outflow boundary points are regarded as flooded topological nodes and associated with the attribute values ​​of the flood-prone area.

[0067] 3) Since this method does not consider the impact of river blockage, when it is difficult to collect urban pipe network distribution and drainage capacity data, the drainage capacity coefficient per unit area per unit time D k (Unit: L·m -2 ·s -1 ) to solve the problem of water receding in flooded areas.

[0068] Drainage capacity per unit area D k =αP 设计频率

[0069] in:

[0070] P 设计频率： Unit: mm. It is the designed drainage capacity of the urban pipe network, which is generally related to the intensity of urban rainstorms. In most cities, it occurs once every 2-5 years.

[0071] α is the reduction coefficient of the drainage capacity of the urban pipeline network. Generally, as time goes by, the urban underground pipeline network will become silted up and the drainage capacity will be reduced, and the actual drainage capacity is only a part of the designed drainage capacity. The reduction coefficient of the drainage capacity of the urban pipeline network α = actual drainage capacity of the urban pipeline network p / design drainage capacity of the urban pipeline network P. In the case of no data, α can be taken as 0.7-1.0 according to the time when the drainage pipeline network was built. After accumulating a certain amount of observation data, it will be corrected based on the actual situation.

[0072] Actual drainage volume of flooded area W v,t =D k ×T×A / 1000

[0073] W v,t : The amount of water discharged from the flooded area A in t time, in cubic meters (m 3 );

[0074] T: drainage duration, in seconds (s);

[0075] A: The area of ​​the flooded area at time t, in square meters (m 2 ).

[0076] Step 3: Perception data integration: integrating rainfall station monitoring data, radar echo map data collection, and forecast rainfall grid integration.

[0077] This step mainly integrates the monitoring data of rain gauge stations, radar echo map data collection, and forecast rainfall grid integration.

[0078] Data sources include:

[0079] Rainfall station monitoring data: obtain rainfall monitoring data from the real-time rainfall database of the hydrological department.

[0080] Radar echo map: obtain real-time radar echo map in the area from the meteorological department, including precipitation intensity, coverage, etc.

[0081] Forecast rainfall grid: Obtain forecast rainfall grid results from the meteorological department.

[0082] To ensure the timeliness of data integration, the corresponding data integration program should refresh data from the data source once every 1-2 minutes to obtain the latest data.

[0083] The integration of rain gauges requires timely analysis of minute-level rainfall data through short-term refreshes so that it can be adapted and integrated with the 6-minute-level radar echo data.

[0084] Radar echo acquisition requires storing the identified dBZ data as a fixed-structure radar echo grid under the premise of vector registration.

[0085] The main purpose of rainfall forecast grid collection 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.

[0086] Step 4: Real-time rainfall grid fusion: perform rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map.

[0087] This step is to perform rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map.

[0088] The rainfall grid for each period is obtained based on the radar echo map, real-time data of the rain gauge station, the rain gauge station layer and the virtual station layer.

[0089] Radar echo recognition: If only radar echo image data is available, the recognition program can be used to parse the radar echo image into a radar echo (dBZ) data grid by color adaptation.

[0090] Things to note during the processing: removal of clutter at low dBZ values, such as considering dBZ>15 as a valid value; and geographic registration issues of radar echo maps.

[0091] 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.

[0092] Note:

[0093] 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.

[0094] The measured data of the rain gauge station may be earlier than the radar echo data. When matching the radar echo data and the measured rainfall data, the necessary time offset correction should be made according to Δt. Considering the time it takes for rain to fall from the clouds to the ground, about 3-5 minutes is appropriate.

[0095] When sampling the radar echo data corresponding to rainfall in minute periods, the start and end time points of the rainfall period should correspond to the period average of dBZ. That is, the regression fitting relationship between rainfall and radar echo is established through multiple historical minute-level rainfall periods and period dBZ averages:

[0096] Assume that the rainfall in the period is P i,t The sample set is

[0097] A={P 1,t ,…,P n-1,t ,P n,t}

[0098] The mean value of radar echo in the time period R i,t-Δt The sample set is

[0099] B={R 1,t-Δt ,…,P n-1,t-Δt ,P n,t-Δt}

[0100] The rainfall during this period

[0101] P t =f A,B (R t-Δt )

[0102] 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.

[0103] 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.

[0104] Longer dBZ sampling time should be considered to improve the rationality of the fitting relationship.

[0105] 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.

[0106] Two-dimensional rainfall fusion interpolation calculation: Generate a two-dimensional interpolated rainfall grid based on the measured rainfall and the virtual station rainfall calculated by regression.

[0107] Step 5, rainfall grid adaptation: bind all the catchment area basins to the rainfall grid structure and associate the corresponding rainfall grid indexes to facilitate the rapid extraction of basin rainfall data.

[0108] The rainfall adaptive index dataset is obtained through the rainfall grid structure information, the watershed grid of the rain-collecting area, and the topological layer of the rain-collecting area. The rainfall adaptive index dataset is the rainfall grid index dataset of each watershed in the rain-collecting area.

[0109] The rainfall grid adaptation includes: 1. Traversing the watershed unit watershed contained in the rainwater collection area, counting the grid units corresponding to the rainfall in the watershed, and forming the rainfall grid unit index and weight value pair data. 2. Packing and combining all the rainfall adaptation data in the rainwater collection area into a rainfall adaptation index dataset.

[0110] The structure of the rainfall adaptation index dataset is explained as follows: one rainfall adaptation index dataset corresponds to multiple rainwater catchment areas, one rainwater catchment area corresponds to multiple sub-basin units, and one sub-basin unit corresponds to multiple rainfall grid unit values.

[0111] Step 6: Real-time rainfall extraction: Extract the average rainfall series of all flooded watersheds.

[0112] This step is to extract the average rainfall series of the sub-basin unit.

[0113] The rainfall sequence set in the rain-collecting area is obtained through the rainfall adaptation index dataset and the real-time rainfall grid sequence.

[0114] The rain collection area rainfall sequence set stores multiple rain collection areas, one rain collection area corresponds to multiple sub-basins, and one sub-basin corresponds to one rainfall sequence.

[0115] Rainfall extraction is to extract rainfall sequence sets for each rain-collecting area one by one according to the rainfall adaptation index dataset.

[0116] Step 7: Forecast rainfall extraction: Extract the average rainfall sequence of the sub-basin unit from the forecast rainfall source.

[0117] This step is to extract the forecast average rainfall sequence of the sub-basin unit from the forecast rainfall source.

[0118] A rainfall sequence set of a rain-collecting area is obtained through a rainfall adaptation index data set and a forecast rainfall grid sequence; the rain-collecting area rainfall sequence set stores a plurality of rain-collecting areas, one rain-collecting area corresponds to a plurality of sub-basins, and one sub-basin corresponds to a rainfall sequence.

[0119] Rainfall extraction is also based on the rainfall adaptation index data set, extracting rainfall sequence sets for each rain-collecting area one by one.

[0120] Step 8: Load and parse the input file, build the internal data structure, and create the output raster template.

[0121] This step is the initialization before calculation.

[0122] The contents loaded in the input file include: DEM of cut but unfilled depressions, rainfall sequence set of rainwater collection area, distribution layer of flood-prone areas, topological layer of rainwater collection area, watershed layer of flooded water system, index dataset of flooded grid filling and flooded risk level index.

[0123] Constructing the internal data structure includes: constructing DEM raster data objects, constructing the rain-collecting area topological tree structure, and constructing the sub-basin watershed topological tree structure within the rain-collecting area.

[0124] Creating a result output grid template includes: using the DEM grid object as a structural module and constructing a result output grid cache space.

[0125] Step 9, rainfall accumulation calculation: Based on the accumulation basin topology, perform accumulation analysis calculations one by one from top to bottom, and output the accumulation analysis calculation results.

[0126] The rainfall inundation calculation includes:

[0127] Benchmark drainage loss deduction: Benchmark drainage loss deduction is performed on the rainfall data within each calculation step.

[0128] Calculation of water drainage and water withdrawal in flooded areas: Calculate the water withdrawal in the flooded areas according to the area drainage coefficient.

[0129] Calculation of overflow water volume in flooded areas: When the amount of flooded water exceeds the critical water volume for overflow, the overflow calculation is started and the overflow water volume can be transferred to the downstream flooded areas.

[0130] ① Convert the rainfall in the calculation step length after deducting the loss of the benchmark drainage in the catchment area into water volume W L,t , superimposed with the original water volume W in this area t-1 , upstream overflow water volume W U,t , and deduct the drainage water volume W in this area v,t Then, the total water volume W in the flooded area within the current calculation step is obtained. t .Right now:

[0131] W t =W L,t +W U,t +W 0,t-1 -W v,t

[0132] ② When the water level in the flooded area exceeds the dry-wet boundary of the outlet (e.g. 0.02m), that is, the flooded water volume exceeds the overflow critical water volume W c When the generalized overflow coefficient K w Deduct the water volume in this area and transfer this part of the overflow water to the downstream flooded area.

[0133] W t =W t -K w ·(W t -W c )

[0134] Step 10: Flooding risk assessment: Assess flooding risk based on flooding risk level indicators.

[0135] This step is to evaluate the flooding risk according to the flooding risk level index. For example, according to the definition of the flooding risk level index, when the maximum water depth of the grid unit does not exceed 15cm, the unit is risk-free; 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, 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, otherwise it is level 4; 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 2; 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.

[0136] The basic data of the present invention include:

[0137] (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.

[0138] (2) River basin data. Mainly includes rivers (water systems) in and around the city, river control section layers and supporting information data.

[0139] (3) Station data. Mainly includes river water level stations and rainfall stations in the urban area and surrounding areas and supporting information data.

[0140] (4) Data on disaster-affected protected areas. Mainly including flood-prone areas, urban roads, bus stops, underground parking lots, underground passages (including subway entrances), government agency locations, schools, hospitals and other important areas, facilities and units.

[0141] (5) Model data: mainly including the structural data of the model and the results data of the model calculation.

[0142] (6) Perception data: Mainly includes rainfall monitoring data, radar echo data, etc.

[0143] (7) Forecast data: Mainly including meteorological rainfall forecast grid data.

[0144] The above-mentioned solution of the present invention can also realize the following steps:

[0145] Step 11: Warning display: including basic information base map display, integrated data information browsing display, flooding risk assessment display, flooding risk association display and warning release, etc.

[0146] The basic information base map display uses a GIS base map to display the urban and surrounding rainfall stations, flood-prone areas, and major river systems, and uses this as a framework container for the entire urban flood risk warning application to expand and implement 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 a rainfall station.

[0147] Integrated data information browsing display; overlaying and displaying real-time rainfall grid and radar echo map on the map, and can be played back in time through the time axis; can realize the display of real-time monitoring data and time series process data of rainfall stations and water level stations.

[0148] Flooding risk assessment display: The two-dimensional flooding time series dynamic effects and risk area rendering results calculated by the model at different time batches 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 areas based on the flooding situation.

[0149] Flooding risk association display: Display the disaster-prone objects associated with flooding risk based on the map. When displaying disaster-prone objects with flooding risk, it is necessary to provide risk level prompts, support comparison between the flooding characteristic water level of the disaster-prone object and the simulated flooding water level, and describe the flooding risk of the disaster-prone protection object.

[0150] The main content of warning release is to generate warning briefings. The warning briefings 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 district, and the flooding risk situation of the disaster-bearing protection objects. The warning release personnel can edit and confirm the generated warning briefings. The warning briefings can be used as reference auxiliary materials for reporting to the superior competent department. The warning briefings can be exported as documents, or sent to other application systems through the integrated third-party warning release interface.

[0151] Step 12: Provide relevant data services: including structural data query service, scheme information query service, scheme results query service and flooding risk related information query service.

[0152] The structural data query service is mainly responsible for querying information such as the grid structure information of the model area, the flooded topological structure, and the flooded area grid filling index dataset.

[0153] The solution information query service mainly provides query of calculation solution batches, input information and control parameters.

[0154] The solution result query service mainly provides raster data and vector distribution data of two-dimensional water depth time series, two-dimensional maximum water depth and two-dimensional flooding duration for query.

[0155] The flooding risk-related information query service mainly provides query on the disaster protection object information associated with the batch scheme simulation results.

Claims

1. A method for early warning of urban flooding risk based on the fusion of radar and ground precipitation, characterized by: The method comprises: Step 1: terrain analysis and calculation; Step 2: Based on the results of terrain analysis, conduct flood-prone area analysis and flooding grid filling analysis, and generate flooding area watershed topology; Step 3: Integrate the monitoring data of rainfall stations, radar echo map data collection and forecast rainfall grid integration; Step 4: Perform rainfall fusion interpolation based on the measured rainfall at the rain gauge station and the radar echo map; Step 5: Rainfall grid adaptation; Step 6, extracting the average rainfall series of all flooded watersheds; Step 7, extracting the average rainfall sequence of the sub-basin unit from the forecast rainfall source; Step 8: Load and parse the input file, build the internal data structure and create the output grid template; Step 9: Calculation of rainfall accumulation; Step 10: Flooding risk assessment and early warning display.

2. The urban flooding risk early warning method based on radar and ground precipitation fusion according to claim 1 is characterized by: The terrain analysis calculation includes: river channel depiction, DEM clipping, low-lying land analysis, terrain filling analysis and terrain confluence analysis; the river channel depiction is based on the actual water system vector line as a template, and the water system is forced to be depicted on the DEM; the DEM clipping is to spatially clip the DEM with the water system surface layer; the water surface clipping area is regarded as a non-flooded area, as the end point of two-dimensional confluence, and no downstream waterlogging occurs; the low-lying land analysis performs terrain filling processing on the basis of the clipped DEM, and marks the lowest point of the filling area with an area greater than the threshold as a depression; the terrain filling analysis performs filling analysis and calculation based on the depicted and clipped DEM terrain; the terrain confluence analysis performs flow direction and flow analysis based on the terrain after filling, and uses 1-2km 2 Generates a catchment-based drainage layer for a baseline threshold.

3. The method for early warning of urban flooding risk based on radar and ground precipitation fusion according to claim 1 is characterized by: The method for analyzing the flood-prone area includes: marking the exit point of the depression based on the location of the depression and the DEM and flow direction grid after filling, and traversing the depression to generate the flood-prone area distribution layer; marking the outflow boundary point based on the flow direction grid; splitting the basic water system according to the exit point of the depression to form a confluence water system layer, and generating the supporting water system watershed grid and layer at the same time; constructing the topological relationship of the rainwater collection area from the depression exit to the outflow boundary point; generating the rainwater collection area grid based on the topological relationship of the rainwater collection area, and establishing the association relationship between the rainwater collection area and the internal water system watershed; the flooding grid filling analysis includes: for each rainwater collection area, starting from the depression, the gradient index is constructed from low to high for the grid cells in the domain according to a certain gradient, so as to improve the unit speed when doing flooding filling; for the rainwater collection area topological layer: Mark the depression outlet and outflow boundary points and the upstream and downstream topological relationships; The exit points of the depression and the boundary points of the two-dimensional outflow are regarded as flooded topological nodes, and the attribute values ​​of the flood-prone area are associated with them; By generalizing the drainage capacity coefficient per unit area per unit time D k ; D k =αP 设计频率 W v，t =D k ×T×A / 1000 W v,t T is the amount of water discharged from the flooded area A in time t, and A is the area of ​​the flooded area at time t.

4. The urban flooding risk early warning method based on radar and ground precipitation fusion according to claim 1 is characterized by: The integrated rain gauge monitoring data is parsed to obtain minute-level rainfall data; Radar echo map data acquisition stores the identified dBZ data as a fixed-structure radar echo grid under the premise of vector registration; forecast rainfall grid integration downloads the latest forecast rainfall grid to the local server in real time for temporary caching.

5. The urban flooding risk early warning method based on radar and ground precipitation fusion according to claim 1 is characterized by: The rainfall fusion interpolation method based on the measured rainfall at the rain gauge station and the radar echo map includes: obtaining the time period rainfall grid based on the radar echo map, the real-time data of the rain gauge station, the rain gauge station layer and the virtual station layer; parsing the radar echo image into the radar echo (dBZ) data grid by color adaptation using a recognition program; sampling the dBZ value at each rain gauge station location, using the regression equation to fit the relationship between the radar echo intensity and the measured rainfall value for this period, and converting the time period dbZ mean value of the corresponding position of the virtual station into the time period rainfall; generating a two-dimensional interpolation rainfall grid based on the measured rainfall and the virtual station rainfall calculated by regression.

6. The method for early warning of urban flooding risk based on radar and ground precipitation fusion according to claim 1 is characterized by: The rainfall grid adaptation is to obtain a rainfall adaptation index dataset through rainfall grid structure information, watershed grid of the rain-collecting area and topological layer of the rain-collecting area. The rainfall adaptation index dataset is a rainfall grid index dataset of each watershed in the rain-collecting area. The rainfall grid adaptation method comprises: traversing the watershed of the basin unit contained in the rain-collecting area, counting the grid cells corresponding to the rainfall in each grid cell in the watershed, and forming rainfall grid cell index and weight value pair data; packaging and combining all rainfall adaptation data in the rain-collecting area into a rainfall adaptation index dataset.

7. The urban flooding risk early warning method based on radar and ground precipitation fusion according to claim 1 is characterized by: The method for extracting the average rainfall sequence of all flooded watersheds includes: obtaining a rainfall sequence set of a rain-collecting area through a rainfall adaptation index data set and a real-time rainfall grid sequence; and extracting a rainfall sequence set for each rain-collecting area one by one according to the rainfall adaptation index data set.

8. The method for early warning of urban flooding risk based on radar and ground precipitation fusion according to claim 1 is characterized by: The method for extracting the average rainfall sequence of sub-basin units from the forecast rainfall source includes: obtaining a rainfall sequence set for a rain-collecting area through a rainfall adaptation index data set and a forecast rainfall grid sequence, and extracting a rainfall sequence set for each rain-collecting area one by one according to the rainfall adaptation index data set.

9. The method for early warning of urban flooding risk based on radar and ground precipitation fusion according to claim 1 is characterized by: The rainfall accumulation calculation includes: Benchmark drainage loss deduction: Benchmark drainage loss deduction is performed on the rainfall data within each calculation step; Calculation of water drainage and water withdrawal in flooded areas: Calculate the water withdrawal in flooded areas according to the area drainage coefficient; Calculation of overflow water volume in flooded areas: When the flooded water volume exceeds the critical overflow water volume, the overflow calculation is started and the overflow water volume is transferred to the downstream flooded areas; Convert the rainfall in the calculation step after deducting the loss of the benchmark drainage in the catchment area into water volume W L,t , superimposed with the original water volume W in this area t-1 , upstream overflow water volume W U,t , and deduct the drainage water volume W in this area v,t Then, the total water volume W in the flooded area within the current calculation step is obtained. t Right now: IN t =In L,t +W U,t +W 0,t-1 -IN v,t When the water level in the flooded area exceeds the dry-wet boundary of the outlet, that is, the flooded water volume exceeds the overflow critical water volume W c When the generalized overflow coefficient K w Deduct the water volume in this area and transfer this overflow water to the downstream flooded area W t =W t -K w ·(W t -W c )。 10. The method for early warning of urban flooding risk based on radar and ground precipitation fusion according to claim 1 is characterized by: The flooding risk assessment and early warning display method includes: assessing the flooding risk according to the flooding risk level index. According to the definition of the flooding risk level index, when the maximum water depth of the grid unit does not exceed 15cm, the unit has no risk; when the maximum water depth of the grid unit is greater than 15cm and less than 27cm, if the flooding time exceeds 30 minutes, the risk level of the unit is level 4, 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, otherwise it is level 4; 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 2; 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.