CN111915158A - Rainstorm disaster weather risk assessment method, device and equipment based on Flood Area model - Google Patents
Rainstorm disaster weather risk assessment method, device and equipment based on Flood Area model Download PDFInfo
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Abstract
The invention relates to the technical field of disaster assessment, in particular to a rainstorm disaster weather risk assessment method based on a Flood Area model, which comprises the steps of collecting and arranging data and building a risk database; calculating the surface rainfall through forecast data, radar estimation and rainfall station data; determining the disaster critical precipitation; analyzing the exposure and the vulnerability of the bearing body; combining a risk database, disaster-causing rainfall determination and exposure and vulnerability technologies of a bearing body, and outputting a disaster risk range and distribution diagram and a disaster risk quantitative estimation result through Flood Area simulation; the rainfall forecasting method is based on the Flood Area model, combines numerical values of various forecasting models, forecasting data and statistical analysis of observation data of the automatic meteorological station, utilizes rainfall forecasting data of various mature numerical modes, improves rainstorm monitoring and forecasting capacity, and lays a foundation for evaluation of rainstorm disastrous weather.
Description
Technical Field
The invention relates to the technical field of disaster assessment, in particular to a rainstorm disaster weather risk assessment method, device and equipment based on a Flood Area model.
Background
Rainstorm is one of the most serious and frequently-occurring disasters in meteorological disasters, easily causes disasters such as flood and urban waterlogging, and further causes casualties and major economic losses, quantitative rainwater estimation is one of hot spots and difficulties in weather research at home and abroad, radar prediction can pass through high-time high-altitude resolution's rainfall intensity information, but its precision is limited, rain gauge measurement is the most widely used simple method for directly measuring precipitation, but site density is limited, in order to improve the estimation precision of rainwater, the existing technical means is mainly to estimate the weather risk of rainstorm disasters through main forecast data, automatic station actual measurement, radar estimation precipitation, and the main deficiency of the estimation technology is as follows: (1) the accuracy of the forecast data is influenced by various factors such as the stability of the model and the limitation of analysis factors; (2) the automatic stations are not uniformly distributed, and the spatial distribution of rainfall cannot be well reflected in a large range. (3) The coverage area of rainfall estimated by the radar is limited, the quality control is difficult, and meanwhile, the rainfall estimated by the radar is easily influenced by terrain shielding and the like.
Disclosure of Invention
The invention aims to provide a rainstorm disaster weather risk assessment method, device and equipment based on a Flood Area model, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a rainstorm disaster weather risk assessment method, device and equipment based on a Flood Area model comprises the following steps:
s1: collecting and arranging data, and building a risk database;
s2: calculating the surface rainfall through forecast data, radar estimation and rainfall station data;
s3: determining the disaster critical precipitation;
s4: analyzing the exposure and the vulnerability of the bearing body;
s5: and (3) combining a risk database, disaster-causing rainfall determination and exposure and vulnerability technologies of a bearing body, and outputting a disaster risk range and distribution diagram and disaster damage risk quantitative estimation through Flood Area simulation.
Preferably, the data collected and sorted in step S1 mainly includes basic data in the aspects of meteorological data, hydrological and hydraulic data, urban pipe network data, geographic information data and social statistics data.
Preferably, the step of calculating the rain amount in step S2 is as follows:
s201: analyzing the spatial correlation of the forecast data, the rainfall station data and the radar station data, and selecting the station data with a larger Moran's I index;
s202: adopting a plurality of spatial interpolation methods to interpolate discrete station data to generate finely meshed planar data, and constructing a spatial interpolation model by adopting the altitude, the topographic relief and the gradient as cooperative variables according to the precipitation spatial distribution characteristics;
s203: and comparing actual effects of different methods and models by adopting a cross validation method, selecting an optimal processing scheme from the actual effects, and performing optimal calculation and gridding calculation on the surface rainfall.
Preferably, the interpolation method used in step S202 is a kriging method and an inverse distance weighting method.
Preferably, the step of determining the disaster critical precipitation in step S3 is as follows:
s301: defining the rainstorm intensity and calculating the frequency and the reappearance period of the rainstorm intensity;
s302: establishing a quantitative relation between rainfall and rainfall, hydrological characteristics and an underlying surface in a rainstorm process surface in a research area;
s303: and calculating the rainfall of the disaster-causing critical surface based on the hydrometeorology coupling technology.
Preferably, the steps of the carrier exposure and vulnerability analysis in step S4 are as follows:
s401: through on-site investigation and the communication cooperation with water conservancy and statistical departments, the problems of non-uniform spatial scale and data fusion are solved by utilizing multiple buffer area analysis and spatial superposition spatial analysis technology in combination with a statistical method, and a multi-source database containing different types of bearing bodies is established;
s402: associating disaster-causing risks of rainstorm disastrous weather with the bearing bodies, performing space superposition on identification results of dynamic rainfall and bearing body data under a unified data frame, extracting the number and space distribution of the bearing bodies under different rainfall precipitation levels and duration in real time, and realizing dynamic identification of exposure of the rainstorm bearing bodies;
s403: performing spatial superposition on the Flood Area simulation result and the data of the bearing bodies, and extracting the exposure of the different types of the bearing bodies at each level of precipitation;
s404: and establishing a response curve of the disaster damage rate of the bearing body along with the change of rainfall, and evaluating the vulnerability of the bearing body.
Preferably, the multi-source database of the bearer in step S401 includes information data of population, economy, buildings, urban traffic, and urban pipe network.
In order to achieve the above object, the present invention further provides a rainstorm disaster weather risk assessment apparatus based on the Flood Area model, including:
the risk database module is used for collecting and storing meteorological data, hydrology and water conservancy data, urban pipe network data, geographic information data and social statistics data;
the surface rainfall calculation module is used for carrying out correlation analysis on the station data, constructing a spatial interpolation model by adopting two methods, and carrying out surface rainfall calculation and gridding calculation by combining a cross validation method;
the disaster critical rainfall capacity determining module is used for determining the rainstorm intensity, calculating the frequency and the recurrence period of the rainstorm intensity, and calculating the rainfall capacity of the disaster critical plane based on the hydrometeorology coupling technology by combining the natural geographic conditions and the disaster prevention engineering facilities in the research area;
the carrier exposure and vulnerability analysis module is used for collecting and establishing a multi-source database containing different types of carriers, realizing dynamic identification of exposure of the heavy rain carriers and extraction of exposure of the different types of carriers at each level of precipitation, and evaluating the vulnerability of the carriers;
and the risk evaluation output module is used for analyzing the risk database, the disaster-causing rainfall and the exposure and the vulnerability of the bearing body, and outputting the disaster risk range and distribution diagram and the disaster risk quantitative estimation.
To achieve the above object, the present invention further provides a rainstorm disaster weather risk assessment device based on the Flood Area model, which includes a processor, a memory, and a computer program, wherein the computer program is stored in the memory and executed by the processor, and the processor can implement the above risk assessment method when executing the computer program.
Compared with the prior art, the invention has the beneficial effects that:
(1) based on a Flood Area model, combining numerical values of various forecasting models, forecasting data and statistical analysis of observation data of an automatic meteorological station, improving a rainstorm disaster risk assessment method and technology, interpolating discrete station data to generate fine gridded planar data, and scientifically and accurately monitoring and assessing a rainstorm process;
(2) by utilizing various mature numerical model rainfall forecast data, the rainstorm monitoring and forecasting capacity is improved;
(3) by using various interpolation methods and checking, the rainfall on the inner surface of the area can be accurately and quickly obtained, and a foundation is laid for the evaluation of the rainstorm disaster weather.
Drawings
Fig. 1 is a schematic flow chart of a risk assessment method provided in embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of a risk assessment apparatus according to embodiment 2 of the present invention;
fig. 3 is a schematic flowchart of step S2 of the risk assessment method according to embodiment 1 of the present invention;
fig. 4 is a schematic flow chart of a risk assessment method S3 according to embodiment 1 of the present invention;
fig. 5 is a schematic flow chart of a risk assessment method S4 according to embodiment 1 of the present invention;
fig. 6 is a schematic working diagram of a risk assessment method according to embodiment 1 of the present invention;
FIG. 7 is a schematic diagram of Kriging spatial autocorrelation analysis;
FIG. 8 is a graph of power function P values versus threshold range determination for the inverse distance weighting method;
FIG. 9 is a schematic diagram illustrating the principle of Flood Area confluence calculation;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, so that those skilled in the art can implement the technical solutions in the embodiments of the present invention with reference to the description text.
Example 1
Referring to fig. 1, the present embodiment provides a rainstorm disaster weather risk assessment method based on a Flood Area model, including the following steps:
s1: collecting and arranging data, and constructing a risk database.
The data collected and sorted in the step S1 mainly include meteorological data, hydrological and hydraulic data, urban pipe network data, geographic information data, and basic data in the aspect of social statistics data, such as DEM, residential distribution, and pipe network.
S2: and calculating the surface rainfall through forecast data, radar estimation and rainfall station data. The method mainly comprises the following steps:
referring to fig. 3, spatial correlation of forecast data, rainfall station data, and radar station data is first analyzed, and station data having a large Moran's I index is selected, and the Moran's I index of each data is calculated according to the following formula.
Wherein n is the total number of data, ziIs the deviation of the observed value of the ith data from the mean value, zjThe same process is carried out; w is ai,jAre the weights of spatial units i and j.
As can be seen from the Moran's I index, Moran's I is an index representing the degree of spatial autocorrelation of the entire region, and the value thereof is between-1 and + 1. The positive value indicates that the data has positive spatial correlation, the larger the value is, the more close the relationship among the cells is, the negative value indicates that the data is abnormal in space, the closer the value is to-1, the larger the difference among the cells is or the distribution is less concentrated, a batch of station data with larger correlation can be selected according to the Moran's I index of each station data, the data with larger difference can be eliminated, and the data processing amount is reduced.
And then, interpolating discrete station data by adopting various spatial interpolation methods to generate fine gridded planar data, and constructing a spatial interpolation model by adopting the altitude, the topographic relief and the gradient as cooperative variables according to the precipitation spatial distribution characteristics, wherein the interpolation methods used are a kriging method and an inverse distance weight method, and the specific use methods of the kriging method and the inverse distance weight method are mainly briefly introduced below.
The kriging method is a relatively sophisticated and highly accurate interpolation method, which is a statistical process for generating an estimated surface from a set of scattered points with z-values, and is based on a statistical model containing autocorrelation (i.e., statistical relationships between measurement points). Kriging assumes that the distance or direction between sample points may reflect a spatial correlation that may be used to account for surface variations. The kriging tool may fit a mathematical function to a specified number of points or all points within a specified radius to determine an output value for each location. The method comprises a plurality of steps, mainly comprising exploratory analysis of data, modeling of a variation function, creation of a surface and research of a variance surface.
The kriging method is a method for performing unbiased optimal linear estimation by using a variation function according to data variation characteristics and then obtaining a point to be measured by weighted average of known sampling points.
The kriging method has the following calculation formula:
wherein, z' (x)0) Is a point x to be measured0The interpolation result of (2); z (x)i) For a sample point x within the investigation regioniThe measured value of (d); n is the known number of samples; lambda [ alpha ]iIs a Krigin weight coefficient, λiThe function of variation is used to determine the discomfort which depends directly on the distance between the sample point and the point to be measured.
The variogram is the basis of the kriging interpolation, and the variogram of the regional variables of the region under study is defined as follows:
according to O-Kriging unbiased and optimal conditions, an equation set is obtained by sorting:
wherein, gamma (x)i,xj) Is a function of variation between sample points, xi,,xjI, j sample points, gamma (x)i,x0) Is a sample point x to be measured0λ is the kriging weight coefficient and μ is the lagrange multiplier.
Referring to the matching situation of a certain point and all other measurement positions in fig. 7, the kriging method executes the process for each measurement point, obtains the principle that objects at a close distance are more similar to objects at a far distance through the kriging method spatial autocorrelation analysis, then weights the surrounding discrete station data to obtain station data at unmeasured positions, and interpolates to generate fine gridded planar data.
Inverse distance weight method
The inverse distance weighting method assumes that each measurement point has a local effect, which decreases with increasing distance. This method is called the inverse distance weight method because it assigns a greater weight to the point closest to the predicted position, but the weight decreases as a function of distance.
The inverse distance weighting method is proposed according to the first law of geography, i.e. the principle of similarity, i.e. objects distributed in space are related to each other, and the similarity between objects close to each other is greater than the similarity between objects far away from each other. The principle is that the attribute value of the point to be measured is the weighted average of the attribute values of the samples in the neighborhood, and the weight is related to the distance between the point to be measured and the samples. The formula of the inverse distance weight method is as follows:
wherein, z' (x)0) Is the point x to be measured0The interpolation result of (2); z (x)i) For a sample point x within the investigation regioni(i is 1,2 …, n); n is the number of participating interpolation points; di0To predict point x0With each known sample point xiThe distance between them; p is the power of the distance. The power function commonly used at present is used to derive the relationship between weight and distance, i.e. the weight is proportional to the inverse distance raised to the power of p. Generally, p is 2 as a default value, and it can be known from fig. 8 that the preferable range of clinical domains is obtained after the power function p value is obtained as a proper value, and the p value can be adjusted by statistical analysis and cross-validation information when used.
The relation between the weight and the distance is obtained by an inverse distance weight method, then the discrete station data around is weighted to estimate the station data of the unmeasured position, and the finely gridded planar data is generated by interpolation.
And finally, comparing actual effects of different methods and models by adopting a cross validation method, selecting an optimal processing scheme from the actual effects, and performing surface rainfall calculation and gridding calculation.
In the given modeling samples, most samples are taken out for modeling, a small part of samples are reserved for forecasting by using the just-built models, the forecasting errors of the small part of samples are solved, and the square sum of the small part of samples is recorded. The method firstly assumes that a certain station point value is unknown, estimates the point by using the measured data of the surrounding stations through space interpolation, changes the stations in turn, and obtains measured values corresponding to the measured values one by one after the steps are repeated, thereby ensuring that each station in a research area can obtain the measured values only once.
The Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (r) of the measured and predicted values were used as the test criteria for the results. r can reflect the correlation degree between the measured value and the predicted value, MAE can estimate the possible error range of the predicted value, RMSE can reflect the estimated sensitivity and extremum effect using the sampling data, and the calculation formula is as follows:
wherein R isMSERepresenting the root mean square error, MAEDenotes mean absolute error, x0Is the measured value of the ith station,
is the average of measured values, xiIs a predictive value for the ith station,
the average n, which is a predicted value, is the number of inspection sites.
Taking a typical rainstorm disaster process as an example, a spatial gridding scheme of rainfall and a plane rainfall calculation method are discussed by adopting different spatial interpolation methods based on hourly site data, and the plane rainfall is calculated by comparing different methods and a better method.
S3: and determining the disaster critical precipitation. The method mainly comprises the following steps:
referring to fig. 4, the intensity of the rainstorm is first defined and the frequency and the recurrence period of the intensity of the rainstorm are calculated, and the formula of the rainstorm is defined as:
wherein q is the rainstorm intensity [ unit L/(s.hm)2)]P is a reproduction period (unit: a) and the value range is 0.25-100 a; t is the duration of rainfall (unit: min), and the value range is 1-120 min. The longer the period of recurrence, the shorter the duration, the greater the intensity of the storm, and A1And b, C, n are parameters related to local rainstorm characteristics and requiring solution: a. the1Designing rainfall (mm) for the rainfall parameter, namely 1min when the recurrence period is 1 a; c is a rainfall variation parameter; b is a rainfall duration correction parameter (unit: min); n is the rainstorm decay index, associated with the recurrence period.
The frequency of the intensity of the rainstorm is calculated according to the following formula:
where Pl is frequency, N is total number of samples (N is data age length), and M is serial number of samples (samples are sorted from large to small).
The rainstorm intensity recurrence period P refers to the average time, in years, that equal or exceed its rainstorm intensity occurs once. From this, the recurrence period calculation formula is derived:
according to the formula, the rainstorm intensity can be clarified, and the frequency and the reappearance period of the rainstorm intensity are calculated.
Then, a quantitative relation between the rainfall of the rainstorm process surface in the research area and the rainfall, hydrological characteristics and underlying surface is established, a plurality of factors of weather, landform and underlying surface in the occurrence of the rainstorm disaster weather in the area are brought into disaster critical conditions of the rainstorm disaster, and various factors in the area influence the dynamic value of the rainfall calculation of the surface.
And finally, calculating the rainfall of the disaster-causing critical plane based on the hydrometeorology coupling technology under the conditions of the rainstorm intensity, the frequency and the recurrence period of the rainstorm intensity and the dynamic value of the rainfall calculation of various factor influence surfaces in the region.
S4: carrier exposure and vulnerability were analyzed. The method mainly comprises the following steps:
referring to fig. 5, firstly, through on-site investigation and general cooperation with water conservancy and statistical departments, the problems of non-uniform spatial scale and data fusion are solved by using multiple buffer analysis and spatial superposition spatial analysis technologies in combination with a statistical method, and a multi-source database containing different types of carriers is established, wherein the multi-source database of the carriers contains information data of population, economy, buildings, urban traffic and urban pipe networks.
Secondly, associating disaster-causing risks of rainstorm disastrous weather with the carrying bodies, carrying out space superposition on the identification result of the dynamic rainfall and the carrying body data under a unified data frame, extracting the quantity and the space distribution of the carrying bodies under different rainfall precipitation levels and duration in real time, and realizing dynamic identification of the exposure of the rainstorm carrying bodies.
And then, performing spatial superposition on the Flood Area simulation result and the data of the bearing bodies, and extracting the exposure of the different types of the bearing bodies at each level of precipitation.
And finally, establishing a response curve of the disaster damage rate of the bearing body along with the change of rainfall, and evaluating the vulnerability of the bearing body.
S5: and (3) combining a risk database, disaster-causing rainfall determination and exposure and fragile technology of a bearing body, and simulating and outputting a disaster risk range and distribution diagram and disaster damage risk quantitative estimation through the Flood Area.
The Flood inundation simulation adopts a Flood Area two-dimensional non-constant flow hydrodynamic model, calculation is based on a hydrodynamic method, specific simulation evolution takes a grid as a unit, eight units around the grid are considered at the same time, the water flow widths of adjacent units are considered to be equal, the units located on diagonal lines are calculated by different length algorithms, and the Flood Area calculation principle is shown in 9.
The effusion quantity of the adjacent units is calculated by a Manning-Stricker formula, the submerging depth of water flow is the difference value between the height of a submerging water level and the height of the ground, the water flow direction in the submerging process is determined by the slope direction of the terrain, the slope direction reflects the direction faced by a slope, the projection of the normal vector of a tangent plane of a point on the ground surface in the slope direction on the horizontal plane and the included angle of the due north direction passing the point represent the maximum change direction of the height value change quantity of the point, and the calculation formula is as follows:
in the formula: alpha is the terrain slope;
elevation change rate in north and south directions;
the elevation change rate in the north-south direction.
Example 2
Referring to fig. 2, the present embodiment provides a rainstorm disaster weather risk assessment apparatus based on the Flood Area model, including:
the risk database module is used for collecting and storing meteorological data, hydrology and water conservancy data, urban pipe network data, geographic information data and social statistics data;
the surface rainfall calculation module is used for carrying out correlation analysis on the station data, constructing a spatial interpolation model by adopting two methods, and carrying out surface rainfall calculation and gridding calculation by combining a cross validation method;
the disaster critical rainfall capacity determining module is used for determining the rainstorm intensity, calculating the frequency and the recurrence period of the rainstorm intensity, and calculating the rainfall capacity of the disaster critical plane based on the hydrometeorology coupling technology by combining the natural geographic conditions and the disaster prevention engineering facilities in the research area;
the carrier exposure and vulnerability analysis module is used for collecting and establishing a multi-source database containing different types of carriers, realizing dynamic identification of exposure of the heavy rain carriers and extraction of exposure of the different types of carriers at each level of precipitation, and evaluating the vulnerability of the carriers;
and the risk evaluation output module is used for analyzing the risk database, the disaster-causing rainfall and the exposure and the vulnerability of the bearing body, and outputting the disaster risk range and distribution diagram and the disaster risk quantitative estimation.
Example 3
The embodiment provides a rainstorm disaster weather risk assessment device based on a Flood Area model, which comprises a processor, a memory and a computer program, wherein the computer program is stored in the memory and is run by the processor, and the processor can realize the risk assessment method when executing the computer program.
In summary, the invention is based on the Flood Area model, combines the numerical values of various forecast models, forecast data and statistical analysis of observation data of the automatic meteorological station, improves the method and the technology for evaluating the storm disaster risk, interpolates discrete station data to generate fine gridding planar data, and scientifically and accurately monitors and evaluates the storm process; by utilizing various mature numerical model rainfall forecast data, the rainstorm monitoring and forecasting capacity is improved; by using various interpolation methods and checking, the rainfall on the inner surface of the area can be accurately and quickly obtained, and a foundation is laid for the evaluation of the rainstorm disaster weather.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A rainstorm disaster weather risk assessment method based on a Flood Area model is characterized by comprising the following steps:
step S1: collecting and arranging data, and building a risk database;
step S2: calculating the surface rainfall through forecast data, radar estimation and rainfall station data;
step S3: determining the disaster critical precipitation;
step S4: analyzing the exposure and the vulnerability of the bearing body;
step S5: and (3) combining a risk database, disaster-causing rainfall determination and exposure and vulnerability technologies of a bearing body, and outputting a disaster risk range and distribution diagram and disaster damage risk quantitative estimation through Flood Area simulation.
2. The stormwater disaster weather risk assessment method based on the Flood Area model according to claim 1, characterized in that: the data collected and sorted in the step S1 mainly comprises basic data in the aspects of meteorological data, hydrological and hydraulic data, urban pipe network data, geographic information data and social statistics data.
3. The stormwater disaster weather risk assessment method based on the Flood Area model according to claim 1, characterized in that: the step of calculating the rain level in step S2 is as follows:
s201: analyzing the spatial correlation of the forecast data, the rainfall station data and the radar station data, and selecting the station data with a larger Moran's I index;
s202: adopting a plurality of spatial interpolation methods to interpolate discrete station data to generate finely meshed planar data, and constructing a spatial interpolation model by adopting the altitude, the topographic relief and the gradient as cooperative variables according to the precipitation spatial distribution characteristics;
s203: and comparing actual effects of different methods and models by adopting a cross validation method, selecting an optimal processing scheme from the actual effects, and performing surface rainfall calculation and gridding calculation.
4. The stormwater disaster weather risk assessment method based on the Flood Area model according to claim 3, characterized in that: the interpolation method used in step S202 is a kriging method and an inverse distance weighting method.
5. The stormwater disaster weather risk assessment method based on the Flood Area model according to claim 1, characterized in that: the step of determining the disaster critical precipitation in step S3 is as follows:
s301: defining the rainstorm intensity and calculating the frequency and the reappearance period of the rainstorm intensity;
s302: establishing a quantitative relation between rainfall and rainfall, hydrological characteristics and an underlying surface in a rainstorm process surface in a research area;
s303: and calculating the rainfall of the disaster-causing critical surface based on the hydrometeorology coupling technology.
6. The stormwater disaster weather risk assessment method based on the Flood Area model according to claim 1, characterized in that: the steps of the carrier exposure and vulnerability analysis in step S4 are as follows:
s401: through on-site investigation and the communication cooperation with water conservancy and statistical departments, the problems of non-uniform spatial scale and data fusion are solved by utilizing multiple buffer area analysis and spatial superposition spatial analysis technology in combination with a statistical method, and a multi-source database containing different types of bearing bodies is established;
s402: associating disaster-causing risks of rainstorm disastrous weather with the bearing bodies, performing space superposition on identification results of dynamic rainfall and bearing body data under a unified data frame, extracting the number and space distribution of the bearing bodies under different rainfall precipitation levels and duration in real time, and realizing dynamic identification of exposure of the rainstorm bearing bodies;
s403: performing spatial superposition on the Flood Area simulation result and the data of the bearing bodies, and extracting the exposure of the different types of the bearing bodies at each level of precipitation;
s404: and establishing a response curve of the disaster damage rate of the bearing body along with the change of rainfall, and evaluating the vulnerability of the bearing body.
7. The stormwater disaster weather risk assessment method based on the Flood Area model according to claim 6, characterized in that: the multi-source database of the bearer in step S401 includes information data of population, economy, buildings, urban traffic, and urban pipe network.
8. The stormwater disaster weather risk assessment method based on the Flood Area model according to claim 1, characterized in that: and (3) simulating and outputting disasters including waterlogging disasters by the Flood Area in the step S5, wherein the waterlogging simulation adopts a Flood Area two-dimensional unsteady flow hydrodynamic model.
9. The utility model provides a rainstorm disaster weather risk assessment device based on Flood Area model which characterized in that: the method comprises the following steps:
the risk database module is used for collecting and storing meteorological data, hydrology and water conservancy data, urban pipe network data, geographic information data and social statistics data;
the surface rainfall calculation module is used for carrying out correlation analysis on the station data, constructing a spatial interpolation model by adopting two methods, and carrying out optimal surface rainfall calculation and gridding calculation by combining a cross validation method;
the disaster critical rainfall capacity determining module is used for determining the rainstorm intensity, calculating the frequency and the recurrence period of the rainstorm intensity, and calculating the rainfall capacity of the disaster critical plane based on the hydrometeorology coupling technology by combining the natural geographic conditions and the disaster prevention engineering facilities in the research area;
the carrier exposure and vulnerability analysis module is used for collecting and establishing a multi-source database containing different types of carriers, realizing dynamic identification of exposure of the heavy rain carriers and extraction of exposure of the different types of carriers at each level of precipitation, and evaluating the vulnerability of the carriers;
and the risk evaluation output module is used for analyzing the risk database, the disaster-causing rainfall and the exposure and the vulnerability of the bearing body, and outputting the disaster risk range and distribution diagram and the disaster risk quantitative estimation.
10. A stormwater disaster weather risk assessment device based on the Flood Area model, comprising a processor, a memory and a computer program stored in the memory and executed by the processor, characterized in that: the processor, when executing the computer program, is capable of implementing the risk assessment method of any of the above claims 1 to 8.
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