Multi-scale rainfall data-based rainstorm flood disaster assessment method and system

Abstract

The invention relates to the technical field of rainstorm and flood disaster assessment, and particularly provides a rainstorm and flood disaster assessment method and system based on multi-scale rainfall data, and the method comprises the steps: obtaining a digital elevation model of a target region, calculating a terrain curvature distribution field and an elevation mutation characteristic field, a terrain vertical gradient zone is obtained, and multiple layers of ground precipitation observation station point groups are arranged; generating a vertical weight factor according to observation station altitude distribution, and combining a radar beam direction and a terrain slope to generate a slope direction shielding coefficient; fusing ground-based radar, satellite remote sensing and ground station data, and generating a terrain optimization precipitation field through a vertical weight factor and a slope shielding coefficient; and inputting the precipitation field into the terrain coupling flood model, and deducing disaster space distribution in combination with a water flow path network and a soil infiltration correction mechanism. According to the method, the problem of observation distortion caused by terrain shielding and rainfall vertical differentiation in alpine and valley areas is solved, and the precision and reliability of rainstorm flood disaster assessment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rainstorm and flood disaster assessment, and in particular to a rainstorm and flood disaster assessment method and system based on multi-scale precipitation data. Background Art

[0002] Storm and flood assessment is a core technical tool in disaster prevention and mitigation. In areas with complex terrain, insufficiently accurate precipitation data can hinder assessment accuracy. Existing technologies are unable to address the following issues in mountainous and canyon regions with sharply dissected terrain: First, mountains physically block ground-based radar beams, creating persistent blind spots in canyons and steep slopes. Second, existing technologies are not layered according to terrain gradients, failing to capture sudden vertical changes in precipitation, leading to underestimation of storm intensity at low-altitude stations.

[0003] For the special landform of high mountain canyons, there is an urgent need for a rainstorm and flood disaster assessment method and system based on multi-scale precipitation data to solve the problem of insufficient precipitation observation accuracy caused by terrain shielding and vertical differentiation. Summary of the Invention

[0004] (1) Technical problems to be solved The purpose of the present invention is to provide a method and system for rainstorm and flood disaster assessment based on multi-scale precipitation data to solve the problem of precipitation observation distortion caused by terrain shielding and vertical differentiation in mountainous canyon areas.

[0005] (2) Technical solution To achieve the above objectives, the present invention provides a method for rainstorm and flood disaster assessment based on multi-scale precipitation data, the method comprising: Step S1: Obtain a digital elevation model of the target area and extract grid point elevation values; calculate the terrain curvature distribution field and the elevation mutation characteristic field based on the grid point elevation values; perform terrain segmentation based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field, and identify the terrain vertical gradient belt; and deploy a multi-layer surface precipitation observation station group within the terrain vertical gradient belt.

[0006] Step S2: obtaining site-scale precipitation observation values ​​and their three-dimensional spatial coordinates through the multi-layer ground precipitation observation site group; generating three-dimensional spatial information of terrain based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters; obtaining ground-based radar monitoring data and satellite remote sensing precipitation data, and combining them with the site-scale precipitation observation values ​​to construct a multi-scale precipitation dataset; generating a vertical weight factor based on the altitude distribution of the three-dimensional spatial coordinates; generating a slope shielding coefficient based on the geometric relationship between the radar beam direction vector and the digital elevation model slope normal vector; and performing weighted fusion on the multi-scale precipitation dataset in combination with the vertical weight factor and the slope shielding coefficient to generate a terrain-optimized precipitation field.

[0007] Step S3: inputting the terrain-optimized precipitation field into the terrain-coupled flood model, performing flood simulation calculations based on the water flow path network and soil infiltration correction mechanism of the digital elevation model; and outputting a disaster spatial distribution map of the target area.

[0008] Furthermore, the method for calculating the terrain curvature distribution field and the elevation mutation characteristic field based on the grid point elevation values ​​includes: Obtain the grid point elevation value of the digital elevation model and calculate the grid point elevation value by discrete Laplace operator. The terrain curvature value at the grid point is used to construct the terrain curvature distribution field.

[0009] In the preset In the analysis window, calculate the terrain structure characteristics of the grid point at the center of the window .

[0010] in, is the maximum elevation value in the window, is the minimum elevation value in the window, is the mean gradient along the direction of the maximum precipitation gradient; the continuous area with a terrain curvature value greater than the preset curvature threshold is marked as a candidate gradient belt; the grid point with a terrain structure feature value greater than the preset structure feature threshold is marked as an elevation mutation feature point.

[0011] Furthermore, the method of segmenting the terrain based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field to identify the terrain vertical gradient zone includes: The curvature gradient value of each grid point is calculated according to the terrain curvature distribution field; the connected areas with curvature gradient values ​​greater than the preset candidate gradient threshold are extracted and marked as candidate gradient belts; the candidate gradient belts are spatially overlaid with the set of elevation mutation feature points. If the density of elevation mutation feature points in the candidate gradient belt exceeds the preset density threshold, it is determined to be a terrain vertical gradient belt.

[0012] Furthermore, the method of deploying a multi-layer surface precipitation observation station group within the terrain vertical gradient zone includes: All grid points within the vertical gradient zone of the terrain are obtained, and an ordered grid point sequence is generated from low to high altitude; the vertical stratification boundary is determined according to the ratio of the cumulative value of the terrain curvature value, and the ordered grid point sequence is divided into L layers.

[0013] The average terrain curvature of each vertical layer is calculated, and the number of sites in each layer is dynamically allocated according to the ratio of the average terrain curvature value to the reference terrain curvature value; if the total number of sites exceeds the preset maximum number of sites, the number of sites in each layer is compressed proportionally.

[0014] Furthermore, the method of generating terrain three-dimensional spatial information based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters includes: Taking the three-dimensional coordinates of the ground observation site P as the reference point, connect the radar station position S and P to generate the beam propagation path ; Extract the area with P as the center and radius on the surface of the digital elevation model The circular analysis area is discretized into radial paths, each path with The angle is ,in is the radar beam half-power angle.

[0015] Check the intersection of each radial path and the digital elevation model surface. If the intersection exists on the propagation path between point P and the radar station position S, and the elevation value of the intersection is greater than the elevation value of the radar beam straight propagation path at that location, it is marked as a blocked path; count the number of blocked paths ; Calculate the terrain shielding parameters ,in, is the standard deviation of elevation within the circular analysis area, is the standard deviation of the benchmark terrain, Terrain complexity adjustment parameter.

[0016] Furthermore, the method of generating a vertical weight factor according to the altitude distribution of the three-dimensional space coordinates; and generating a slope shielding coefficient according to the geometric relationship between the radar beam direction vector and the slope normal vector of the digital elevation model includes: Get the measured altitude of the site , the vertical weight factor is obtained by the weight calculation formula ,in, is the base weight, is the vertical attenuation coefficient, The regional base altitude.

[0017] Calculate the terrain tangent plane equation at the site according to the digital elevation model and obtain the normal vector And normalized to a unit vector ,in, is the central differential gradient in the x direction, is the central differential gradient in the y direction.

[0018] Calculate radar beam direction vector ;calculate With unit vector Sine of the angle ; Generate aspect shielding coefficient ,in, is the masking nonlinear adjustment index.

[0019] Furthermore, the method of weightedly fusing the multi-scale precipitation dataset by combining the vertical weight factor and the aspect shielding coefficient to generate a terrain-optimized precipitation field includes: Resample the ground-based radar monitoring data and satellite remote sensing precipitation data in the multi-scale precipitation dataset to the target spatial resolution grid to generate a spatially aligned radar precipitation field. and satellite precipitation fields ; Generate ground precipitation field by performing inverse distance weighted interpolation on station-scale precipitation observations ; According to the vertical weight factor Slope aspect shading coefficient Calculate terrain physical weight coefficient ; According to the weight relationship Perform fusion calculation to output terrain optimized precipitation field; among them, is the radar reliability coefficient, is the satellite timeliness coefficient.

[0020] Furthermore, the method of inputting the terrain-optimized precipitation field into a terrain-coupled flood model and performing flood deduction calculations based on a water flow path network and a soil infiltration correction mechanism of a digital elevation model includes: The surface slope value of each grid cell is calculated based on the grid point elevation value, and the runoff equation is selected according to the comparison result between the surface slope value and the preset slope threshold.

[0021] Obtain soil type spatial distribution data and calculate the infiltration capacity correction coefficient ,in, is the saturated moisture content, is the soil water suction, is the saturated hydraulic conductivity, is the soil volume moisture content, is the cumulative infiltration volume.

[0022] Iterative calculations are performed within a preset time step. Each time step includes a runoff evolution calculation stage and an infiltration correction stage. The runoff evolution calculation stage updates the grid water depth by adopting the motion wave equation or diffusion wave equation. The infiltration correction stage adjusts the infiltration water volume according to the infiltration capacity correction coefficient. When the change in grid water depth between two adjacent iterations is less than the preset tolerance threshold, the calculation is terminated and the final grid water depth is output.

[0023] Furthermore, the method of outputting the disaster spatial distribution map of the target area includes: Obtain the grid cell flooding depth output by the terrain-coupled flood model With water flow speed ;when When It is determined to be the second risk level: It is determined to be the third risk level: When the risk level is determined to be the fourth level, is the first water depth threshold, is the second water depth threshold, is the first flow rate threshold, is the second flow rate threshold.

[0024] The risk level determination rules are applied to all grid cells to generate a disaster risk level coding matrix, which is then converted into a disaster risk level map with geographic coordinate references; the disaster risk level map includes four attribute fields: spatial location, inundation depth, water flow velocity, and risk level.

[0025] Based on the same inventive concept, on the other hand, the present invention also provides a rainstorm flood disaster assessment system based on multi-scale precipitation data, the system comprising: The terrain perception observation network construction module is used to obtain the digital elevation model of the target area and extract the grid point elevation values; calculate the terrain curvature distribution field and the elevation mutation characteristic field based on the grid point elevation values; segment the terrain based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field, and identify the terrain vertical gradient belt; and deploy a multi-layer ground precipitation observation station group within the terrain vertical gradient belt.

[0026] A multi-source precipitation physical fusion module is used to obtain site-scale precipitation observations and their three-dimensional spatial coordinates through the multi-layer ground precipitation observation station group; generate three-dimensional terrain information based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters; obtain ground-based radar monitoring data and satellite remote sensing precipitation data, and construct a multi-scale precipitation dataset in combination with the site-scale precipitation observations; generate a vertical weight factor based on the altitude distribution of the three-dimensional spatial coordinates; generate a slope shielding coefficient based on the geometric relationship between the radar beam direction vector and the digital elevation model slope normal vector; and perform weighted fusion of the multi-scale precipitation dataset in combination with the vertical weight factor and the slope shielding coefficient to generate a terrain-optimized precipitation field.

[0027] The terrain coupling deduction module is used to input the terrain-optimized precipitation field into the terrain coupling flood model, perform flood deduction calculations based on the water flow path network and soil infiltration correction mechanism of the digital elevation model, and output the disaster spatial distribution map of the target area.

[0028] (3) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: 1. This invention accurately identifies key gradient zones of vertical precipitation mutations through terrain curvature segmentation, and dynamically deploys observation stations in the curvature mutation zone to solve the problem of insufficient detection accuracy caused by the rough altitude stratification of traditional methods.

[0029] 2. The present invention establishes a geometric shielding model between the radar beam and the slope surface, physically quantifies the shielding effect of the mountain on the radar signal, significantly eliminates the radar blind spots caused by the canyon terrain, and realizes the full-area and accurate capture of precipitation data in complex terrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a method for assessing rainstorm and flood disasters based on multi-scale precipitation data according to Example 1 of the present invention; Figure 2 This is a schematic diagram of the composition of a rainstorm and flood disaster assessment system based on multi-scale precipitation data according to Example 2 of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] Before giving examples, it is necessary to explain the application scenarios of the present invention. The present invention is particularly suitable for high mountain canyon areas with dramatic terrain fluctuations and significant vertical precipitation differentiation. These areas are significantly shielded by the terrain, and the angle between the canyon direction and the radar beam is less than When the observation blind area ratio exceeds 80%; when the vertical differentiation rate of precipitation is greater than In the high mountain canyon area, the traditional observation network does not follow the terrain gradient, resulting in large errors in disaster assessment. The present invention uses the vertical gradient band identification technology based on terrain curvature segmentation; uses the radar beam and slope surface for geometric modeling to eliminate the impact of radar blind spots in the canyon area. The canyon area selected in the embodiment is a typical scene of this type, and its terrain undulation intensity coefficient is The area is highly undulating, which verifies the effectiveness of the present invention in extreme terrain.

[0033] Example 1: Figure 1 As shown, this embodiment provides a method for assessing rainstorm and flood disasters based on multi-scale precipitation data, the method comprising: Step S1: Obtain a digital elevation model of the target area and extract grid point elevation values; calculate the terrain curvature distribution field and the elevation mutation characteristic field based on the grid point elevation values; perform terrain segmentation based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field, and identify the terrain vertical gradient belt; and deploy a multi-layer surface precipitation observation station group within the terrain vertical gradient belt.

[0034] Step S2: obtaining site-scale precipitation observation values ​​and their three-dimensional spatial coordinates through the multi-layer ground precipitation observation site group; generating three-dimensional spatial information of terrain based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters; obtaining ground-based radar monitoring data and satellite remote sensing precipitation data, and combining them with the site-scale precipitation observation values ​​to construct a multi-scale precipitation dataset; generating a vertical weight factor based on the altitude distribution of the three-dimensional spatial coordinates; generating a slope shielding coefficient based on the geometric relationship between the radar beam direction vector and the digital elevation model slope normal vector; and performing weighted fusion on the multi-scale precipitation dataset in combination with the vertical weight factor and the slope shielding coefficient to generate a terrain-optimized precipitation field.

[0035] Step S3: inputting the terrain-optimized precipitation field into the terrain-coupled flood model, performing flood simulation calculations based on the water flow path network and soil infiltration correction mechanism of the digital elevation model; and outputting a disaster spatial distribution map of the target area.

[0036] For example, taking a canyon as an example, a digital elevation model of the target area with a resolution of 30m is obtained.

[0037] Furthermore, the method for calculating the terrain curvature distribution field and the elevation mutation characteristic field based on the grid point elevation values ​​includes: Obtain the grid point elevation value of the digital elevation model and calculate the grid point elevation value by discrete Laplace operator. The terrain curvature value at the grid point is used to construct the terrain curvature distribution field.

[0038] In the preset In the analysis window, calculate the terrain structure characteristics of the grid point at the center of the window .

[0039] in, is the maximum elevation value in the window, is the minimum elevation value in the window, is the mean gradient along the direction of the maximum precipitation gradient; the continuous area with a terrain curvature value greater than the preset curvature threshold is marked as a candidate gradient belt; the grid point with a terrain structure feature value greater than the preset structure feature threshold is marked as an elevation mutation feature point.

[0040] For example, a 3×3 grid window is taken , the center point coordinates are , the elevation value is .

[0041] Calculate terrain curvature values .

[0042] Elevation extreme value , mean slope ; .

[0043] Specifically, we set the curvature threshold to 400 and the structural feature threshold to 5.0. , , it is marked as a feature point.

[0044] Furthermore, the method of segmenting the terrain based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field to identify the terrain vertical gradient zone includes: The curvature gradient value of each grid point is calculated according to the terrain curvature distribution field; the connected areas with curvature gradient values ​​greater than the preset candidate gradient threshold are extracted and marked as candidate gradient belts; the candidate gradient belts are spatially overlaid with the set of elevation mutation feature points. If the density of elevation mutation feature points in the candidate gradient belt exceeds the preset density threshold, it is determined to be a terrain vertical gradient belt.

[0045] For example, the central difference method calculates the curvature gradient value G=104.2, and identifies three connected regions with G>100.

[0046] Area A , containing 82 mutation points, the density is 16.4 points / , greater than the preset density threshold of 10 points / ,reserve.

[0047] Furthermore, the method of deploying a multi-layer surface precipitation observation station group within the terrain vertical gradient zone includes: All grid points within the vertical gradient zone of the terrain are obtained, and an ordered grid point sequence is generated from low to high altitude; the vertical stratification boundary is determined according to the ratio of the cumulative value of the terrain curvature value, and the ordered grid point sequence is divided into L layers.

[0048] The average terrain curvature of each vertical layer is calculated, and the number of sites in each layer is dynamically allocated according to the ratio of the average terrain curvature value to the reference terrain curvature value; if the total number of sites exceeds the preset maximum number of sites, the number of sites in each layer is compressed proportionally.

[0049] For example, the six grid points in the terrain vertical gradient zone are arranged in ascending order of altitude [2850, 2920, 3150, 3200, 3420, 3520]; the corresponding terrain curvature values ​​are [180, 290, 380, 410, 380, 420], and the total number of layers L = 3. .

[0050] Layer 1 Boundary: , ; .

[0051] Layer 2 Boundary: , ; .

[0052] Layer 1 average , layer 2 average , layer 3 average The average of the three layers is ; , , The total number of stations is 31>30, and the new .

[0053] Furthermore, the method of generating terrain three-dimensional spatial information based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters includes: Taking the three-dimensional coordinates of the ground observation site P as the reference point, connect the radar station position S and P to generate the beam propagation path ; Extract the area with P as the center and radius on the surface of the digital elevation model The circular analysis area is discretized into radial paths, each path with The angle is ,in is the radar beam half-power angle.

[0054] Check the intersection of each radial path and the digital elevation model surface. If the intersection exists on the propagation path between point P and the radar station position S, and the elevation value of the intersection is greater than the elevation value of the radar beam straight propagation path at that location, it is marked as a blocked path; count the number of blocked paths ; Calculate the terrain shielding parameters ,in, is the standard deviation of elevation within the circular analysis area, is the standard deviation of the benchmark terrain, Terrain complexity adjustment parameter.

[0055] For example, a 3D terrain surface was generated based on a 30-meter digital elevation model, with the coordinates of a specific station serving as a control point. A propagation path SP from radar station S to station P was simulated. Path SP was detected to be interrupted by a mountain ridge 565 meters from point P. The intersection elevation of 3250 meters was greater than the theoretical elevation of line SP of 3010 meters, marking it as an obscured path. Paths within a radius of r = 565 meters were counted, resulting in a total of 360 paths and 331 obscured paths.

[0056] Regional elevation standard deviation , is the dimensionless coefficient calibrated by radar echo experiment, is the standard deviation of the reference terrain, and the terrain shielding parameter .

[0057] Furthermore, the method of generating a vertical weight factor according to the altitude distribution of the three-dimensional space coordinates; and generating a slope shielding coefficient according to the geometric relationship between the radar beam direction vector and the slope normal vector of the digital elevation model includes: Get the measured altitude of the site , the vertical weight factor is obtained by the weight calculation formula ,in, is the base weight, is the vertical attenuation coefficient, The regional base altitude.

[0058] Calculate the terrain tangent plane equation at the site according to the digital elevation model and obtain the normal vector And normalized to a unit vector ,in, is the central differential gradient in the x direction, is the central differential gradient in the y direction.

[0059] Calculate radar beam direction vector ;calculate With unit vector Sine of the angle ; Generate aspect shielding coefficient ,in, is the masking nonlinear adjustment index.

[0060] For example, the regional base elevation is obtained by calculating the arithmetic mean of the digital elevation model. , the altitude of a site , then the absolute height difference .

[0061] Setting Base Weights , vertical attenuation coefficient , then the vertical weight factor .

[0062] Among them, the vertical attenuation coefficient is the correction strength of the altitude bias on the precipitation observation weight.

[0063] , Terrain tangent plane normal vector , normalized , beam direction vector , calculate the sine of the angle , .

[0064] The shielding nonlinear adjustment index is calibrated through radar echo attenuation experiments, with a value range of 1.0~1.5. ; Get the slope shielding coefficient .

[0065] Furthermore, the method of weightedly fusing the multi-scale precipitation dataset by combining the vertical weight factor and the aspect shielding coefficient to generate a terrain-optimized precipitation field includes: Resample the ground-based radar monitoring data and satellite remote sensing precipitation data in the multi-scale precipitation dataset to the target spatial resolution grid to generate a spatially aligned radar precipitation field. and satellite precipitation fields ; Generate ground precipitation field by performing inverse distance weighted interpolation on station-scale precipitation observations ; According to the vertical weight factor Slope aspect shading coefficient Calculate terrain physical weight coefficient ; According to the weight relationship Perform fusion calculation to output terrain optimized precipitation field; among them, is the radar reliability coefficient, is the satellite timeliness coefficient.

[0066] For example, the radar and satellite data are resampled to a 1 km grid, and the site data are interpolated using inverse distance weighted interpolation to generate the surface precipitation field.

[0067] Set radar reliability coefficient when real-time radar signal-to-noise ratio>35dB , set the satellite timeliness coefficient when GPM data delay is <30 minutes , terrain physical weight coefficient ; Radar precipitation values , satellite precipitation values , ground interpolated precipitation value ;but ; Output terrain optimized precipitation field.

[0068] Furthermore, the method of inputting the terrain-optimized precipitation field into a terrain-coupled flood model and performing flood deduction calculations based on a water flow path network and a soil infiltration correction mechanism of a digital elevation model includes: The surface slope value of each grid cell is calculated based on the grid point elevation value, and the runoff equation is selected according to the comparison result between the surface slope value and the preset slope threshold.

[0069] Obtain soil type spatial distribution data and calculate the infiltration capacity correction coefficient ,in, is the saturated moisture content, is the soil water suction, is the saturated hydraulic conductivity, is the soil volume moisture content, is the cumulative infiltration volume.

[0070] Iterative calculations are performed within a preset time step. Each time step includes a runoff evolution calculation stage and an infiltration correction stage. The runoff evolution calculation stage updates the grid water depth by adopting the motion wave equation or diffusion wave equation. The infiltration correction stage adjusts the infiltration water volume according to the infiltration capacity correction coefficient. When the change in grid water depth between two adjacent iterations is less than the preset tolerance threshold, the calculation is terminated and the final grid water depth is output.

[0071] Exemplarily, the precipitation field is input into the flood model, and iterative calculations are performed and the results are output.

[0072] Calculating grid slope Using the wave equation of motion: .

[0073] The soil type is sandy loam, determined according to the USDA soil texture classification, and the water absorption of sandy soil is , saturated moisture content , real-time moisture content , saturated moisture content ,but: ; Perform iterative solution, and the infiltration amount decreases as F increases.

[0074] Set tolerance threshold , the iteration content is as follows:

[0075] Grid water depth after the termination of the iteration , the water flow velocity is obtained by solving the wave equation .

[0076] Furthermore, the method of outputting the disaster spatial distribution map of the target area includes: Obtain the grid cell flooding depth output by the terrain-coupled flood model With water flow speed ;when When It is determined to be the second risk level: It is determined to be the third risk level: When the risk level is determined to be the fourth level, is the first water depth threshold, is the second water depth threshold, is the first flow rate threshold, is the second flow rate threshold.

[0077] The risk level determination rules are applied to all grid cells to generate a disaster risk level coding matrix, which is then converted into a disaster risk level map with geographic coordinate references; the disaster risk level map includes four attribute fields: spatial location, inundation depth, water flow velocity, and risk level.

[0078] Exemplarily, a first water depth threshold is set , the second water depth threshold , first flow rate threshold and the second flow rate threshold .

[0079] and , determined to be the third risk level.

[0080] Comprehensive evaluation of the output: [longitude, latitude, , , third risk level].

[0081] Example 2: Based on the same inventive concept, Figure 2 As shown, this embodiment also provides a rainstorm and flood disaster assessment system based on multi-scale precipitation data, the system comprising: The terrain perception observation network construction module is used to obtain the digital elevation model of the target area and extract the grid point elevation values; calculate the terrain curvature distribution field and the elevation mutation characteristic field based on the grid point elevation values; segment the terrain based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field, and identify the terrain vertical gradient belt; and deploy a multi-layer ground precipitation observation station group within the terrain vertical gradient belt.

[0082] A multi-source precipitation physical fusion module is used to obtain site-scale precipitation observations and their three-dimensional spatial coordinates through the multi-layer ground precipitation observation station group; generate three-dimensional terrain information based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters; obtain ground-based radar monitoring data and satellite remote sensing precipitation data, and construct a multi-scale precipitation dataset in combination with the site-scale precipitation observations; generate a vertical weight factor based on the altitude distribution of the three-dimensional spatial coordinates; generate a slope shielding coefficient based on the geometric relationship between the radar beam direction vector and the digital elevation model slope normal vector; and perform weighted fusion of the multi-scale precipitation dataset in combination with the vertical weight factor and the slope shielding coefficient to generate a terrain-optimized precipitation field.

[0083] The terrain coupling deduction module is used to input the terrain-optimized precipitation field into the terrain coupling flood model, perform flood deduction calculations based on the water flow path network and soil infiltration correction mechanism of the digital elevation model, and output the disaster spatial distribution map of the target area.

[0084] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0085] Finally, it should be noted that although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rainstorm flood disaster assessment method based on multi-scale precipitation data, characterized in that: The method comprises: Obtain a digital elevation model of the target area and extract grid point elevation values; calculate the terrain curvature distribution field and elevation mutation characteristic field based on the grid point elevation values; segment the terrain based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field to identify terrain vertical gradient zones; and deploy a multi-layer surface precipitation observation station cluster within the terrain vertical gradient zones; Obtaining site-scale precipitation observations and their three-dimensional spatial coordinates through the multi-layer ground precipitation observation site group; generating three-dimensional spatial information of terrain based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters; acquiring ground-based radar monitoring data and satellite remote sensing precipitation data, and combining them with the site-scale precipitation observations to construct a multi-scale precipitation dataset; generating a vertical weight factor based on the altitude distribution of the three-dimensional spatial coordinates; generating a slope shielding coefficient based on the geometric relationship between the radar beam direction vector and the digital elevation model slope normal vector; and performing weighted fusion on the multi-scale precipitation dataset in combination with the vertical weight factor and the slope shielding coefficient to generate a terrain-optimized precipitation field. The terrain-optimized precipitation field is input into a terrain-coupled flood model, and flood simulation calculations are performed based on the water flow path network and soil infiltration correction mechanism of the digital elevation model; and a disaster spatial distribution map of the target area is output.

2. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 1, characterized in that: The method for calculating the terrain curvature distribution field and the elevation mutation characteristic field according to the grid point elevation values ​​includes: Obtain the grid point elevation value of the digital elevation model and calculate the grid point elevation value by discrete Laplace operator. The terrain curvature value at the grid point is used to form the terrain curvature distribution field; In preset In the analysis window, calculate the terrain structure characteristics of the grid point at the center of the window ; in, is the maximum elevation value in the window, is the minimum elevation value in the window, is the mean gradient along the direction of the maximum precipitation gradient; the continuous area with a terrain curvature value greater than the preset curvature threshold is marked as a candidate gradient belt; the grid point with a terrain structure feature value greater than the preset structure feature threshold is marked as an elevation mutation feature point.

3. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 2, characterized in that: The method of segmenting the terrain based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field to identify the terrain vertical gradient zone includes: The curvature gradient value of each grid point is calculated according to the terrain curvature distribution field; the connected areas with curvature gradient values ​​greater than the preset candidate gradient threshold are extracted and marked as candidate gradient belts; the candidate gradient belts are spatially overlaid with the set of elevation mutation feature points. If the density of elevation mutation feature points in the candidate gradient belt exceeds the preset density threshold, it is determined to be a terrain vertical gradient belt.

4. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 3, characterized in that: The method for arranging a multi-layer surface precipitation observation station group within the terrain vertical gradient zone includes: Obtain all grid points within the vertical gradient zone of the terrain, and generate an ordered grid point sequence from low to high altitude; determine the vertical stratification boundary according to the ratio of the cumulative value of the terrain curvature value, and divide the ordered grid point sequence into L layers; The average terrain curvature of each vertical layer is calculated, and the number of sites in each layer is dynamically allocated according to the ratio of the average terrain curvature value to the reference terrain curvature value; if the total number of sites exceeds the preset maximum number of sites, the number of sites in each layer is compressed proportionally.

5. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 4, characterized in that: The method of generating terrain three-dimensional spatial information according to the digital elevation model and the three-dimensional spatial coordinates and obtaining terrain shielding parameters includes: Taking the three-dimensional coordinates of the ground observation site P as the reference point, connect the radar station position S and P to generate the beam propagation path ; Extract the area with P as the center and radius on the surface of the digital elevation model The circular analysis area is discretized into radial paths; Check the intersection of each radial path and the digital elevation model surface. If the intersection exists on the propagation path between point P and the radar station position S, and the elevation value of the intersection is greater than the elevation value of the radar beam straight propagation path at that location, it is marked as a blocked path; count the number of blocked paths ; Calculate the terrain shielding parameters ,in, is the standard deviation of elevation within the circular analysis area, is the standard deviation of the benchmark terrain, Terrain complexity adjustment parameter.

6. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 5, characterized in that: generating a vertical weight factor according to the altitude distribution of the three-dimensional space coordinates; Methods for generating the slope shielding coefficient based on the geometric relationship between the radar beam direction vector and the slope normal vector of the digital elevation model include: Get the measured altitude of the site , the vertical weight factor is obtained by the weight calculation formula ,in, is the base weight, is the vertical attenuation coefficient, is the regional benchmark altitude; Calculate the terrain tangent plane equation at the site according to the digital elevation model and obtain the normal vector And normalized to a unit vector ,in, is the central differential gradient in the x direction, is the central differential gradient in the y direction; Calculate radar beam direction vector ;calculate With unit vector Sine of the angle ; Generate slope aspect shielding coefficient ,in, is the masking nonlinear adjustment index.

7. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 6, characterized in that: The method of weightedly fusing the multi-scale precipitation dataset by combining the vertical weight factor and the aspect shielding coefficient to generate a terrain-optimized precipitation field includes: Resample the ground-based radar monitoring data and satellite remote sensing precipitation data in the multi-scale precipitation dataset to the target spatial resolution grid to generate a spatially aligned radar precipitation field. and satellite precipitation fields ; Generate ground precipitation field by performing inverse distance weighted interpolation on station-scale precipitation observations ; According to the vertical weight factor Slope aspect shielding coefficient Calculate terrain physical weight coefficient ; According to the weight relationship Perform fusion calculation to output terrain optimized precipitation field; among them, is the radar reliability coefficient, is the satellite timeliness coefficient.

8. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 7, characterized in that: The method of inputting the terrain-optimized precipitation field into a terrain-coupled flood model and performing flood deduction calculations based on the water flow path network and soil infiltration correction mechanism of the digital elevation model includes: Calculate the surface slope value of each grid cell based on the grid point elevation value, and select the runoff equation based on the comparison result between the surface slope value and the preset slope threshold; Obtain soil type spatial distribution data and calculate the infiltration capacity correction coefficient ,in, is the saturated moisture content, is the soil water suction, is the saturated hydraulic conductivity, is the soil volume moisture content, is the cumulative infiltration volume; Iterative calculations are performed within a preset time step. Each time step includes a runoff evolution calculation stage and an infiltration correction stage. The runoff evolution calculation stage updates the grid water depth by adopting the motion wave equation or diffusion wave equation. The infiltration correction stage adjusts the infiltration water volume according to the infiltration capacity correction coefficient. When the change in grid water depth between two adjacent iterations is less than the preset tolerance threshold, the calculation is terminated and the final grid water depth is output.

9. The method for rainstorm and flood disaster assessment based on multi-scale precipitation data according to claim 8, characterized in that: The method for outputting the disaster spatial distribution map of the target area includes: Obtain the grid cell flooding depth output by the terrain-coupled flood model With water flow speed ;when When , it is determined to be the first risk level; when It is determined to be the second risk level: It is determined to be the third risk level: When the risk level is determined to be the fourth level, is the first water depth threshold, is the second water depth threshold, is the first flow rate threshold, is the second flow rate threshold; The risk level determination rules are applied to all grid cells to generate a disaster risk level coding matrix, which is then converted into a disaster risk level map with geographic coordinate references; the disaster risk level map includes four attribute fields: spatial location, inundation depth, water flow velocity, and risk level.

10. A rainstorm and flood disaster assessment system based on multi-scale precipitation data, characterized in that: The system comprises: A terrain perception observation network construction module is used to obtain a digital elevation model of the target area and extract grid point elevation values; calculate the terrain curvature distribution field and elevation mutation characteristic field based on the grid point elevation values; perform terrain segmentation based on the spatial coupling relationship between the terrain curvature distribution field and the elevation mutation characteristic field to identify terrain vertical gradient zones; and deploy a multi-layer surface precipitation observation station group within the terrain vertical gradient zones; A multi-source precipitation physical fusion module is configured to obtain site-scale precipitation observations and their three-dimensional spatial coordinates through the multi-layer ground precipitation observation site group; generate three-dimensional terrain information based on the digital elevation model and the three-dimensional spatial coordinates to obtain terrain shielding parameters; obtain ground-based radar monitoring data and satellite remote sensing precipitation data, and construct a multi-scale precipitation dataset by combining them with the site-scale precipitation observations; generate a vertical weight factor based on the altitude distribution of the three-dimensional spatial coordinates; generate an aspect shielding coefficient based on the geometric relationship between the radar beam direction vector and the digital elevation model slope normal vector; and perform weighted fusion of the multi-scale precipitation dataset by combining the vertical weight factor and the aspect shielding coefficient to generate a terrain-optimized precipitation field. The terrain coupling deduction module is used to input the terrain-optimized precipitation field into the terrain coupling flood model, perform flood deduction calculations based on the water flow path network and soil infiltration correction mechanism of the digital elevation model, and output the disaster spatial distribution map of the target area.

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