Rainstorm disaster risk assessment method based on objective identification of rainstorm process and determination of intensity grade, medium and program product

Abstract

The invention discloses a rainstorm disaster risk assessment method based on objective identification of a rainstorm process and determination of an intensity grade, a medium and a program product, and belongs to the technical field of risk assessment, and the method comprises the steps: collecting and preprocessing day-by-day precipitation data of a to-be-calculated station, and forming a long-time sequence precipitation database; the maximum daily precipitation in the minimum historical year is selected as a rainstorm rainfall threshold value, starting and ending dates, the number of process days and the comprehensive strength of the rainstorm process are identified by calculating the maximum equivalent rainstorm intensity of each rainstorm process which reaches or exceeds the rainstorm rainfall threshold value over the years, and then the comprehensive strength of the annual strongest rainstorm process is obtained; then constructing a rainstorm process intensity recurrence period threshold value index by fitting a GEV function of the strongest rainstorm process comprehensive intensity, finally determining a rainstorm process intensity grade according to a recurrence period threshold value, and comprehensively evaluating the rainstorm disaster risk. By objectively identifying the rainstorm process and determining the intensity grade of the rainstorm process, the accuracy of determining the intensity of the rainstorm process can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorological and hydrological rainstorm disaster risk assessment, and relates to the identification of rainstorm processes, the determination of rainstorm intensity levels, and the risk analysis of rainstorm disasters. Specifically, it relates to a rainstorm disaster risk assessment method, medium, and program product that use daily precipitation observation data to identify rainstorm processes and establish a sample sequence of rainstorm process intensities to divide rainstorm process intensities through typical recurrence periods. The application of this technology can improve the objective identification of rainstorm processes and the accuracy of disaster assessment of rainstorm processes. Background Art

[0002] Rainstorm disasters are a serious meteorological disaster that can lead to serious disasters such as urban waterlogging, flooding, dam failures of reservoirs, farmland inundation, and interruptions of transportation and telecommunications, causing serious harm to social economy and people's lives and property. The important disaster-causing factor for rainstorm disasters is the comprehensive intensity of the rainstorm process. Whether the rainstorm process can be accurately and reasonably identified and its intensity level determined is not only a scientific research issue but also directly affects the accuracy of the assessment of the impact of rainstorm disasters, and further affects the scientific prevention and control of rainstorm disasters and the design and operation safety of major projects, urban drainage pipe networks, flood control of water conservancy projects, etc. Therefore, objectively identifying rainstorm processes and determining intensity levels has important theoretical significance and practical application value.

[0003] Rainstorm disaster risk assessment is an important part of disaster prevention and mitigation work, and its core lies in objectively identifying rainstorm processes, quantifying rainstorm intensities, and analyzing their potential impacts. Currently, the identification of rainstorm processes generally relies on simple statistics of daily precipitation and traditional rainstorm intensity threshold standards. Specifically, in the prior art, the identification of rainstorm processes mainly depends on a uniformly set daily precipitation threshold (i.e., daily precipitation is greater than or equal to 50 mm) and continuous precipitation before and after to judge the rainstorm process, and the termination criterion for the rainstorm process is set artificially as no precipitation for 1 to 2 days. However, this national unified rainstorm threshold fails to fully consider the differences in regional climate and terrain. It is too large for arid regions in the north and too small for humid regions in the south. Therefore, using the same rainstorm threshold standard in different regions often has quite different impacts. A daily precipitation of 50 mm in the south may cause little or almost no disaster, while in the north, especially in the northwest, serious disasters may occur. In addition, the method of artificially setting no precipitation for 1 or 2 days to judge the termination of the rainstorm process also has defects and deficiencies, ignoring the continuity of the precipitation process and the actual impact degree. Therefore, the rainstorm processes and intensities identified by the prior art cannot fully reflect the intensity and disaster impact degree of the rainstorm process.

[0004] In addition, the classification of rainstorm intensity levels is a key step in rainstorm disaster risk assessment, directly affecting the scientific nature of disaster prevention and control decisions and engineering designs. In existing technologies, there is a lack of a unified quantitative standard for classifying rainstorm intensity levels, and it is usually roughly classified based on experience or historical data. This method is difficult to meet the refined and scientific requirements for classifying rainstorm disaster risk levels in actual disaster prevention and mitigation work. Especially in fields such as flood warning, urban drainage system design, and flood control scheduling of water conservancy projects, an accurate rainstorm intensity level standard is crucial for improving disaster prevention and control capabilities.

[0005] With the continuous warming of the global climate, extreme rainstorm events are becoming more and more frequent, having an increasing impact on urban operations, transportation, and people's lives. The requirements for classifying rainstorm processes and intensity levels, as well as accurately assessing possible hazards and impacts, have also increased accordingly. Therefore, how to objectively select rainstorm thresholds differentiated according to local historical climate characteristics and objectively and quantitatively determine rainstorm processes and intensity levels, which can effectively improve the accuracy of differentiating rainstorm processes and intensities, thereby more accurately assessing the danger of rainstorms and providing scientific support for rainstorm prevention and reducing disaster impacts, remains an urgent technical problem to be solved currently. Summary of the Invention

[0006] (I) Objectives of the Invention Aiming at the technical defects and deficiencies in the existing determination of rainstorm processes and intensities, such as strong subjectivity and low accuracy in rainstorm process identification, unscientific and unreasonable classification of rainstorm intensity levels, and difficulty in accurately reflecting disaster impacts by rainstorm disaster risk assessment methods, etc., to solve at least one of the above and other technical problems in the existing technology, the objective of the present invention is to provide a rainstorm disaster risk assessment method, medium, and program product based on objectively identifying rainstorm processes and determining intensity levels. It can select the heavy precipitation rainfall threshold according to local climate characteristics to determine the rainstorm threshold, and then objectively count the maximum equivalent rainstorm intensity of each rainstorm process within a year that at least reaches or exceeds the rainstorm threshold to identify the start and end dates, process days, and comprehensive intensity of the rainstorm process. Select the process with the maximum comprehensive intensity of rainstorm processes within a year as the strongest rainstorm process; then fit the probability density function of the comprehensive intensity of the strongest rainstorm process through the Generalized Extreme Value Distribution (GEV) to construct a rainstorm process intensity recurrence period threshold index, and finally determine the rainstorm process comprehensive intensity level according to the recurrence period threshold index value, realizing the objective determination of rainstorm processes and intensity levels, improving the accuracy of determining rainstorm process intensities, enhancing the objectivity and accuracy of rainstorm disaster risk assessment, and providing a scientific basis for flood control and disaster reduction.

[0007] (II) Technical Solutions To achieve the objective of the invention and solve its technical problems, the present invention adopts the following technical solutions: The first invention object of the present invention is to provide a rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels, which is used to objectively identify rainstorm processes based on meteorological observation data, quantify the comprehensive intensity of rainstorms and divide intensity levels, and conduct a more accurate assessment of rainstorm disaster risks, so as to improve the rainstorm disaster warning and prevention capabilities, and provide a scientific basis for reducing rainstorm disaster losses. The method at least includes the following steps when implemented: SS1. Collection and preprocessing of terrain environment and daily precipitation data of the simulated station Collect topographic and environmental information of the simulated stations and daily precipitation data for the past 30 years or more, perform quality control and preprocessing on the original data, remove false values ​​that exceed the climate threshold and rigid values ​​found in the spatiotemporal consistency check, and establish a long-term precipitation database with reliable quality; SS2. Determine the rainstorm threshold of the simulated station through the annual maximum daily precipitation series Based on the long-term precipitation database established in step SS1, the maximum daily precipitation of the simulated station in previous years is counted, and the historical minimum annual maximum daily precipitation is selected as the heavy rain threshold of the simulated station. When the historical minimum annual maximum daily precipitation is less than 25 mm, 25 mm is taken as the heavy rain threshold of the simulated station; SS3. Objectively identify all rainstorm processes at the simulated station and calculate the comprehensive intensity Based on the long-term precipitation database established in step SS1 and the rainstorm threshold determined in step SS2, objectively identify all rainstorm processes in the proposed station over the years and calculate the comprehensive intensity of each rainstorm process. First, starting from the beginning of each year, take whether the daily precipitation reaches or exceeds the rainstorm threshold as the judgment condition, traverse the daily precipitation data in turn and mark all dates that reach or exceed the rainstorm threshold. Then, with each marked date as the center, slide forward and backward day by day to calculate the equivalent rainstorm intensity of the rainstorm process, select the maximum equivalent rainstorm intensity generated in different day combinations as the comprehensive intensity of the rainstorm process, take the period corresponding to the maximum equivalent rainstorm intensity as a complete rainstorm process, and determine its start and end dates and process days, until all marked dates are traversed and all rainstorm processes are identified; SS4. Establish the annual extreme value series of the comprehensive intensity of the rainstorm process at the simulated station and fit the extreme value distribution function Based on all the rainstorm processes in the simulated stations objectively identified in step SS3 and the calculated comprehensive intensity of each rainstorm process, the maximum comprehensive intensity of the rainstorm process in each year is extracted to form the annual extreme value sequence of the comprehensive intensity of the rainstorm processes in the past years. The generalized extreme value distribution function (GEV) is used to fit the annual extreme value sequence, and the scale parameter, location parameter and shape parameter of the GEV function are obtained by linear moment estimation or maximum likelihood estimation; SS5. Comprehensive intensity level standard of rainstorm process corresponding to the key recurrence period of flood level Based on the generalized extreme value distribution function fitted in step SS4, calculate the comprehensive intensity thresholds of rainstorm processes corresponding to the key return periods of flood levels of 5 years, 10 years, 20 years, and 50 years respectively. R 5 , R 10 , R 20 , R 50 , and then divide the comprehensive intensity of the rainstorm process into five levels according to the calculated thresholds. Among them, level 1 with slight impact corresponds to the situation where the comprehensive intensity of the rainstorm process is less than R 5 , level 2 with light impact corresponds to the situation where the comprehensive intensity of the rainstorm process is greater than or equal to R 5 and less than R 10 , level 3 with medium impact corresponds to the situation where the comprehensive intensity of the rainstorm process is greater than or equal to R 10 and less than R 20 , level 4 with heavy impact corresponds to the situation where the comprehensive intensity of the rainstorm process is greater than or equal to R 20 and less than R 50 , level 5 with extremely heavy impact corresponds to the situation where the comprehensive intensity of the rainstorm process is greater than or equal to R 50 ; SS6. Rainstorm disaster risk assessment in the area where the station to be calculated is located According to the comprehensive intensity level standard of the rainstorm process established in step SS5, classify the comprehensive intensity of any rainstorm process objectively identified at the station to be calculated, and at least combine the disaster-causing factor information including the topographic environment information, land use type, population density information, and social and economic conditions in the area where the station to be calculated is located, comprehensively evaluate the disaster risk that the rainstorm process may cause in the area where the station to be calculated is located, and output the rainstorm disaster risk assessment result.

[0008] The second object of the present invention is to provide a computer program product, including computer instructions, and the computer instructions are used to execute the above-mentioned rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels.

[0009] The third object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels is realized.

[0010] (III) Technical effects Compared with the prior art, the rainstorm disaster risk assessment method, medium and program product based on objectively identifying rainstorm processes and determining intensity levels of the present invention have the following beneficial and remarkable technical effects: When determining the rainstorm process and intensity level occurring at the station to be calculated, the present invention first determines its "rainstorm threshold" using the climatic characteristics of the station to be calculated, and then objectively counts the maximum equivalent rainstorm intensity of the precipitation process that reaches or exceeds the "rainstorm threshold" to identify the start and end dates, number of days of the process, and comprehensive intensity of the rainstorm process. Then, select the comprehensive intensity of the strongest rainstorm process in each year, use the generalized extreme value distribution function to construct a recurrence period threshold index for the comprehensive intensity of the rainstorm process, and finally determine the intensity level of the rainstorm process according to the recurrence period threshold index. Thus, it solves the problems in the traditional method that the "rainstorm threshold" has nothing to do with the local climate and the interval time of the interruption of the rainstorm precipitation process is not objective, ensuring the objectivity of the determination of the rainstorm threshold and the rainstorm process. The intensity of the rainstorm process reflects the comprehensive effect of the process rainfall and duration. The determined intensity level has a higher correlation with the rainstorm flood affected area and economic losses than the existing method, and has passed the significance level test ( α = 0.01), and can more accurately reflect the impact degree of the rainstorm disaster. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The figure shows a schematic diagram of the implementation process of the rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels of the present invention; Figure 2 The figure shows a schematic diagram of the daily precipitation of a certain rainstorm process at the Wuhan Station objectively identified by the present invention and the equivalent rainstorm intensity of different combinations of days including the maximum daily precipitation calculated and statistically; Figure 3 The figure shows a schematic diagram of the change of the comprehensive intensity of the annual rainstorm process (new rainstorm intensity index) and the maximum daily precipitation of each year at the Wuhan Station calculated by the method of the present invention; Figure 4 The figure shows a schematic diagram of the comparison of the annual changes of the comprehensive intensity of the rainstorm process (new rainstorm intensity index), the cumulative intensity of the rainstorm process (original rainstorm intensity index), and the rainstorm flood affected area in Hubei Province calculated by the method of the present invention; Figure 5 The figure shows a schematic diagram of the comparison of the standardized annual changes of the comprehensive intensity of the rainstorm process (new rainstorm intensity index), the cumulative intensity of the rainstorm process (original rainstorm intensity index), and the direct economic losses of rainstorm floods in Hubei Province calculated by the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. The described embodiments are part of the embodiments of the present invention, not all of the embodiments, and the described embodiments are exemplary and are intended to be used to explain the present invention, and cannot be understood as limiting the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0013] The present invention aims to provide a rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels, which is used to objectively identify rainstorm processes based on meteorological observation data, quantify the comprehensive intensity of rainstorms and divide intensity levels, and conduct a more accurate assessment of rainstorm disaster risks, so as to improve the rainstorm disaster warning and prevention capabilities, and provide a scientific basis for reducing rainstorm disaster losses.

[0014] Example 1 As a specific example, Figure 1 As shown, the rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels of the present invention first collects and pre-processes precipitation data of the simulated station, determines the rainstorm threshold, objectively identifies all rainstorm processes of the simulated station and determines the comprehensive intensity, establishes an annual extreme value sequence of the comprehensive intensity of the rainstorm process, divides the rainstorm intensity level according to the comprehensive intensity threshold of the return period, and finally performs a rainstorm disaster risk assessment. Specifically, the method includes at least the following steps when implemented: SS1. Collection and preprocessing of terrain environment and daily precipitation data of the simulated station The topographic environment information of the simulated stations and the daily precipitation data for the past 30 years or more are collected, and the original data are quality controlled and preprocessed to eliminate false values ​​that exceed the climate threshold and rigid values ​​found in the spatiotemporal consistency check, so as to establish a long-term precipitation database with reliable quality.

[0015] SS2. Determine the rainstorm threshold of the simulated station through the annual maximum daily precipitation series Based on the long-term precipitation database established in step SS1, the maximum daily precipitation of the simulated station over the years is counted, and the historical minimum annual maximum daily precipitation is selected as the heavy rain threshold of the simulated station. When the historical minimum annual maximum daily precipitation is less than 25 mm, 25 mm is taken as the heavy rain threshold of the simulated station.

[0016] SS3. Objectively identify all rainstorm processes at the simulated station and calculate the comprehensive intensity Based on the long-term precipitation database established in step SS1 and the rainstorm threshold determined in step SS2, objectively identify all the rainstorm processes within each year at the station to be calculated and calculate the comprehensive intensity of each rainstorm process. First, starting from the beginning of each year, using whether the daily precipitation reaches or exceeds the rainstorm threshold as the judgment condition, sequentially traverse the daily precipitation data and mark all the dates that reach or exceed the rainstorm threshold. Then, centered on each marked date, slide forward and backward day by day to calculate the equivalent rainstorm intensity of the rainstorm process, and select the maximum equivalent rainstorm intensity generated in different combinations of days as the comprehensive intensity of the rainstorm process. Take the time period corresponding to the maximum equivalent rainstorm intensity as a complete rainstorm process and determine its start and end dates and the number of days of the process until all marked dates are traversed and all rainstorm processes are identified.

[0017] SS4. Establish the annual extreme value sequence of the comprehensive intensity of the rainstorm process at the station to be calculated and fit the extreme value distribution function Based on all the rainstorm processes within each year at the station to be calculated objectively identified in step SS3 and the comprehensive intensity of each rainstorm process calculated, extract the maximum comprehensive intensity of the rainstorm process within each year to form the annual extreme value sequence of the comprehensive intensity of the rainstorm process. Use the Generalized Extreme Value (GEV) distribution function to fit the annual extreme value sequence, and use the linear moment estimation method to statistically obtain the scale parameter, location parameter, and shape parameter of the generalized extreme value distribution function.

[0018] SS5. Divide the corresponding comprehensive intensity grade standard of the rainstorm process according to the key return periods of flood grades Based on the generalized extreme value distribution function fitted in step SS4, calculate the comprehensive intensity thresholds of the rainstorm process corresponding to the key return periods of flood grades of 5 years, 10 years, 20 years, and 50 years respectively R 5 、 R 10 、 R 20 、 R 50 , and then divide the comprehensive intensity of the rainstorm process into five grades according to the calculated thresholds. Among them, grade 1 with slight influence corresponds to the situation where the comprehensive intensity of the rainstorm process is less than R 5 ; grade 2 with light influence corresponds to the situation where the comprehensive intensity of the rainstorm process is greater than or equal to R 5 and less than R 10 ; grade 3 with medium influence corresponds to the situation where the comprehensive intensity of the rainstorm process is greater than or equal to R 10 and less than R 20 ; grade 4 with heavy influence corresponds to the situation where the comprehensive intensity of the rainstorm process is greater than or equal toR 20 and less than R 50 In the case of Level 5 severe impact, the comprehensive intensity of the rainstorm process is greater than or equal to R 50 The comprehensive intensity level classification and impact degree of rainstorm process are shown in Table 1.

[0019] Table 1 Classification of comprehensive intensity levels and impact levels of rainstorm processes SS6. Risk assessment of rainstorm disasters in the area where the proposed station is located According to the comprehensive intensity grade standard of rainstorm process established in step SS5, the comprehensive intensity of any rainstorm process at the objectively identified simulated station is graded, and at least the disaster-causing factor information including the terrain environment information, land use type, population density information and socio-economic conditions of the area where the simulated station is located is combined to comprehensively evaluate the disaster risk that the rainstorm process may cause in the area where the simulated station is located, and output the rainstorm disaster risk assessment result.

[0020] Example 2 Based on the above-mentioned embodiment 1, as a further refinement and supplement, this embodiment 2 further describes in detail the specific operation steps in step SS1 of the method when collecting and preprocessing the terrain environment and daily precipitation data of the simulated station.

[0021] SS1.1 Collect terrain environment and daily precipitation data of the proposed station, including at least: the longitude, latitude, altitude of the proposed station and the daily precipitation during the period of heavy rain process required for statistics, as well as daily precipitation observation data for the past 30 years or more.

[0022] SS1.2 Preprocess the meteorological observation data collected by the simulated station. First, the false values ​​in the original data that exceed the climate threshold are removed by using the climate threshold of the meteorological variables. For the rigid values ​​that are fixed for multiple consecutive observations, The spatiotemporal consistency check is performed in order to filter and remove the rigid values, where u t Indicates a moment in the meteorological observation data t The observed value of u t+1 express t The observation value at time +1. At the same time, the missing precipitation data are supplemented by interpolation. The climate threshold is determined by statistical analysis of the historical data of the simulated station and its surrounding meteorological stations and dynamically adjusted according to geographical and climate characteristics.

[0023] SS1.3 Standardization and database construction: Based on the algorithm formula, the precipitation data after the above processing is subjected to standardization processing to ensure dimensionless data for subsequent analysis, where X is the original data, is the data mean, σ is the standard deviation, X' is the standardized data. Finally, a long-term precipitation database with reliable quality, no missing values, no false values, and standardized processing is constructed.

[0024] As an option, in sub-step SS1.2, the climate threshold is determined as follows: First, within a range of 100 km around the station to be calculated, several long-term precipitation stations with precipitation probability distributions similar to that of the station to be calculated are selected. The selected stations should have a correlation significance test of precipitation in the past 10 years with the station to be calculated exceeding the 95% confidence level, and the altitude difference from the station to be calculated should not exceed 100 m. Second, the variances of the daily precipitation of the station to be calculated and each selected long-term precipitation station are calculated respectively. Three times the maximum value of the daily precipitation variances of all stations plus the climate average value is taken as the climate threshold of the station to be calculated. Then, based on the determined climate threshold, the original precipitation data of the station to be calculated is checked, and the false values exceeding the climate threshold in the data are removed.

[0025] Example 3 Based on the above Examples 1 and 2, this Example 3 further details the specific operation steps for determining the "rainstorm threshold" of the station to be calculated in step SS2. At the same time, the rainstorm threshold is reasonably set in combination with the statistical sample sequence to ensure the scientific nature and regional applicability of the threshold.

[0026] SS2.1 Based on the meteorological observation data preprocessed in step SS1, using the long-term daily precipitation data of the station to be calculated, statistically establish a sample sequence of the daily maximum precipitation of the station to be calculated for the past 30 years or more.

[0027] SS2.2 Sort the obtained sample sequence of the daily maximum precipitation of each year from smallest to largest, mark the minimum value of the sample sequence, and select the minimum value in the daily maximum precipitation sequence as the "rainstorm threshold" (also known as the "heavy precipitation threshold") of the station to be calculated, and use Z v to represent.

[0028] SS2.3 Determine whether the obtained rainstorm threshold of the station to be calculated is less than 25 mm. If the rainstorm threshold is greater than or equal to 25 mm, directly take this minimum value as the rainstorm threshold of the station to be calculated. If the rainstorm threshold is less than 25 mm, take the rainstorm threshold of the station to be calculated Z v = 25 mm.

[0029] According to the above method, a sample sequence of the maximum daily precipitation over the years can be established, and the minimum and maximum values of the sample sequence of the maximum daily precipitation over the years at each station can be obtained. Table 2 exemplarily gives the rainstorm thresholds of 7 representative stations in several domestic regions. Only the minimum value of the annual maximum daily precipitation at the Yinchuan Station is 11.9 mm, which is less than 25 mm, so its rainstorm threshold is taken as Z v = 25 mm. The minimum values of the annual maximum daily precipitation at other representative stations are all greater than 25 mm, and their minimum values can be directly taken as the "rainstorm threshold".

[0030] Table 2 Minimum, maximum values and rainstorm thresholds of the annual maximum daily precipitation sequence of representative stations in each region Through the above operations, in Example 3, the calculation and determination of the rainstorm threshold are refined and supplemented, ensuring that the threshold can objectively and scientifically reflect the rainstorm characteristics of each representative station and is effectively connected with the subsequent steps of rainstorm process identification and intensity assessment.

[0031] Example 4 On the basis of the above Examples 1 to 3, in this Example 4, the steps of objectively identifying all rainstorm processes and statistically synthesizing the intensity in step SS3 are described in detail.

[0032] SS3.1 Based on the meteorological observation data preprocessed in step SS1, and then using the rainstorm threshold obtained in step SS2 Z v . First, traverse the daily precipitation data of each year in turn, identify and mark all the dates within each year that reach or exceed the rainstorm threshold Z v , and define these dates as rainstorm days; SS3.2 Centering on each marked rainstorm day, slide forward and backward by one day to increase the number of precipitation days until the continuous precipitation does not reach the rainstorm threshold and then terminate, and calculate the equivalent rainstorm intensity of each rainstorm process after increasing by one day respectively Rs , and its calculation formula: In the formula: Rs ( j , n ) is the equivalent rainstorm intensity of the j th rainstorm process with a duration of n days; n is the number of days of the rainstorm process, with the unit of day; a is the weight coefficient, with a value of 0.8; is the average precipitation when the duration within the rainstorm process is n , and ,R i is the precipitation on the i th day during the rainstorm process, i = 1, 2, …, n .

[0033] SS3.3 For each identified potential rainstorm process, by comparing the equivalent rainstorm intensities of different rainstorm process days, the maximum value is taken as the comprehensive intensity of the rainstorm process Rm , and the corresponding Rm day is recorded as the rainstorm process day Rd , and its calculation formula: In the formula: is the maximum value found by comparison within the rainstorm process period for different combinations (i.e., n = 1, 2, …, m ), m is the maximum value of the equivalent rainstorm intensity Rm corresponding to the day.

[0034] SS3.4 The maximum value Rm of the equivalent rainstorm intensity obtained from the above comparison is the comprehensive intensity of the rainstorm process, and the corresponding day m , which is the objectively determined rainstorm process day Rd . At the same time, record the start and end dates of the process corresponding to the maximum value Rm as the start and end dates of the rainstorm process.

[0035] SS3.5 Repeat steps SS3.2 to SS3.4 to traverse all marked rainstorm days, identify all rainstorm processes within each year, and calculate the start and end dates, duration, and comprehensive intensity of each rainstorm process.

[0036] According to the above steps, taking the daily precipitation data of Wuhan Station in 2022 as an example, combined with the determined rainstorm threshold Zv . First, objectively identify the rainstorm processes at Wuhan Station in 2022 (such as Figure 2 ), calculate the equivalent rainstorm intensities of various combinations through the calculation formula of the equivalent rainstorm intensity Rs of the rainstorm process, and determine the maximum value of the equivalent rainstorm intensity and the corresponding start and end dates of the process through the formula . It is determined that the first rainstorm process is from March 16th to March 25th, lasting for 10 days, and the comprehensive intensity Rm is 124.4 mm; the second rainstorm process is from April 22nd to April 28th, lasting for 7 days, and the comprehensive intensity Rm is 113.3 mm; the third rainstorm process is from June 19th to June 28th, lasting for 10 days, and the comprehensive intensity Rmis 120.3 mm; the 4th rainstorm process is from July 17th to July 21st, lasting for 5 days, and the comprehensive intensity Rm is 117.1 mm.

[0037] Figure 2 shows the daily precipitation at Wuhan Station from June 18th to June 29th, 2022, and the equivalent rainstorm intensity calculated for different combinations of days. Through the formula the equivalent rainstorm intensity of different combinations of precipitation matrices for different days is calculated. The red bars in the figure represent the equivalent rainstorm intensity of different combinations of days, and the blue line with an arrow represents the equivalent rainstorm intensity of the combination with a precipitation of 99.8 mm on June 28th, the central day. Among them, 120.3 mm is the maximum value selected through the formula and is the comprehensive intensity of this rainstorm process. Rm Correspondingly, the time period from June 19th to June 28th is the time of this rainstorm process, that is, the objectively identified 3rd rainstorm process in 2022, and it is also the rainstorm process with the strongest comprehensive intensity among the 4 rainstorm processes in 2022.

[0038] Example 5 Based on the above Examples 1 - 4, this Example 5 further details the specific method and calculation process of establishing the annual extreme value sequence of the comprehensive intensity of rainstorm processes at the proposed calculation station and fitting the Generalized Extreme Value Distribution (GEV) in step SS4 of the present invention.

[0039] SS4.1 Extraction and construction of the annual extreme value sequence Based on the comprehensive intensity of each rainstorm process within each year obtained in step SS3, select the maximum comprehensive intensity of the rainstorm process within the year to form the annual extreme value sequence of the comprehensive intensity of rainstorm processes over the years: Among them, Rm ( i ) is the comprehensive intensity of the strongest rainstorm process in the i th year, n is the time span, ensuring that the length of the annual extreme value sequence is not less than 30 years to meet the sample requirements for extreme value distribution statistics. If no rainstorm process meeting the conditions is identified in a certain year, then select the precipitation day with the maximum daily precipitation in that year as the representative of the rainstorm process, and the comprehensive intensity is directly taken as the precipitation value of that day.

[0040] SS4.2 Fitting the Generalized Extreme Value Distribution (GEV) The fitting extreme value distribution function in the embodiment of the present invention refers to fitting the extreme value distribution function - Generalized Extreme Value Distribution (GEV) according to the extreme value distribution theory using the annual extreme value sequence of the comprehensive intensity of rainstorm processes.

[0041] Assume the annual extreme value sequence of the comprehensive intensity of rainstorm processes X1 , X 2 , X 3 , ……, X m are independent random variables following the GEV distribution, and their cumulative distribution function F ( X ), that is, the probability of the variable X < x is: When k ≠ 0, then , and k > 0, -∞ < x ≤ u + a / k ; k < 0, u + a / k ≤ x < + ∞ .

[0042] When k = 0 and - ∞ < x < ∞ ,

[0043] In the formula: k , u , a are the shape parameter, location parameter, and scale parameter respectively, x is a certain threshold (such as the comprehensive intensity of a rainstorm process or the daily maximum precipitation).

[0044] SS4.3 Linear Moment Estimation Method for Calculating Parameters The shape parameter, location parameter, and scale parameter of GEV k , u , a can be calculated using the linear moment estimation method based on statistical samples. The specific calculation steps are as follows: (1) The probability weight moment is defined as follows: In the formula: i , j , k is a real number. When j = k = 0 and l is a non - negative integer, M l,0,0 represents the origin moment of order of the conventional moment method, defined M (k) =M 1,0,k It is a conventional probability weighted rectangular form.

[0045] Arrange the sample sequences in ascending order as x 1 ≤ x 2 ≤…≤ x n , M (k) The unbiased estimator of is: (2) The linear moment is a linear combination of the probability weight moments, expressed as follows: ………… Defining Ratio They are L-variance (L-Cv), L-skewness and L-kurtosis respectively.

[0046] (3) The linear moment estimate of the GEV distribution function parameters is calculated by the following formula: Where: k , u , a They are shape parameter, location parameter and scale parameter respectively.

[0047] According to the above method, we can obtain Figure 3 The maximum daily precipitation and annual extreme value sequence of the comprehensive intensity of the rainstorm process at Wuhan Station are shown in Figure 1. Based on the sample linear moment estimation method of this sequence, the three parameters of the GEV distribution function of Wuhan Station are obtained. a , u , k The strongest rainstorm in Wuhan Station was from June 19 to July 6, 2016, which lasted for 18 days and experienced 4 heavy rainstorms, namely 180.0mm on June 19, 162.8mm on July 1, 153.1mm on July 2, and 241.5mm on July 6; the comprehensive intensity was 480.4mm. The maximum daily precipitation in history was 298.5mm on June 20, 1982.

[0048] Through the above content, Example 5 comprehensively refines step SS4, completes the establishment of the annual extreme value sequence and the GEV distribution fitting, and provides a scientific basis for the risk assessment of rainstorm disasters.

[0049] Example 6 Based on the above-mentioned Embodiments 1 to 5, in this Embodiment 6, the method for dividing the comprehensive intensity grade standard of the corresponding rainstorm process according to the key recurrence period of the flood grade is further described in detail, and it is verified and explained in combination with the actual calculation results.

[0050] 5.1 Basis for dividing the key recurrence period of the flood grade According to the recurrence period of standard flood elements in the "Code for Hydrological Information and Forecasting" (GB / T 22482-2008): floods with a recurrence period less than 5 years are general floods, floods with a recurrence period greater than or equal to 5 years and less than 20 years are relatively large floods, floods with a recurrence period greater than or equal to 20 years and less than 50 years are large floods, and floods with a recurrence period greater than or equal to 50 years are extremely large floods; and the recurrence periods crucial for disaster prevention include: less than 10 years, 10 - 20 years, 20 - 50 years, and more than 50 years. Comprehensively determining, the recurrence period of the comprehensive intensity of the rainstorm process is divided into five grades: less than 5 years is a minor impact, greater than or equal to 5 years and less than 10 years is a small impact, greater than or equal to 10 years and less than 20 years is a medium impact, greater than or equal to 20 years and less than 50 years is a large impact, and greater than or equal to 50 years is an extremely large impact (see Table 1).

[0051] 5.2 Calculation of the recurrence period of the comprehensive intensity of the rainstorm process Based on the three parameters (shape parameter k , location parameter u , and scale parameter a ) of the generalized extreme value distribution of the rainstorm process intensity obtained in step SS4, the comprehensive intensity values T = 5 a , 10 a , 20 a , 50 a ) corresponding to each key recurrence period of the comprehensive intensity of the rainstorm process can be calculated, and the calculation formula is as follows: R ( T ) When k ≠ 0, When k = 0, .

[0052] 5.3 Example verification According to the above method, the three parameters of the GEV distribution of the comprehensive intensity of the rainstorm process are obtained, and the comprehensive intensity thresholds T = 5, 10, 20, 50 a ) corresponding to the comprehensive intensity of the strongest rainstorm processes in each representative station of each region over the years are calculated, as shown in Table 3. For example, the comprehensive intensity thresholds corresponding to each key recurrence period ( R ( T ) of Wuhan Station are respectively T = 5, 10, 20, 50 a)R 5 = 219.5 mm, R 10 = 274.6 mm, R 20 = 335.0 mm, R 50 = 425.8 mm; The comprehensive intensity of the strongest rainstorm over the years is 480.4 mm.

[0053] Table 3 Comprehensive intensity thresholds of the strongest rainstorms over the years and at each key recurrence period for representative stations in the region Through the above content, in this Example 6, the step SS5 is described in detail and verified by examples, systematically completing the classification of the comprehensive intensity level of the rainstorm process, providing data support and method guidance for the subsequent rainstorm disaster risk assessment.

[0054] Example 7 Based on the above Examples 1 - 6, in this Example 7, a detailed description is given of the method for objectively determining the classification of the intensity level of the rainstorm process at the station to be calculated in the above step SS6, and an example description of the level assessment is given in combination with the actual rainstorm process at Wuhan Station in 2016.

[0055] According to the above method, based on the objective identification in step SS2, 4 rainstorm processes and the comprehensive intensity at Wuhan Station in 2016 can be obtained ( Z ), as shown in Table 4. Then, substituting the intensity thresholds of the rainstorm processes at each recurrence period (once in how many years) in Table 3 R 5 , R 10 , R 20 , R 50 into Table 1, it can be determined that the levels and impact levels of the 4 rainstorm processes in 2016 (see Table 4).

[0056] Table 4 Assessment of the levels and impact levels of the 4 rainstorm processes at Wuhan Station in 2016 Through this Example 7, the classification of the intensity level of the rainstorm process and the impact assessment are systematically completed, providing further verification for the results of Example 6 and laying a data foundation for the regional risk analysis and emergency management of rainstorm disasters.

[0057] Example 8 Based on the above Embodiments 1 to 7, in this Embodiment 8, further correlation analysis and effect verification are carried out on the comprehensive intensity of historical rainstorm processes obtained by using the above objective rainstorm process identification and comprehensive intensity determination method of the present invention, the rainstorm flood disasters, and the direct economic losses.

[0058] Representative provinces (autonomous regions) in different regions of the country are selected, including Liaoning in the Northeast, Hebei in the North China, Ningxia in the Northwest, Jiangsu in the East China, Hubei in the Central China, Guizhou in the Southwest, and Guangxi in the South China. The comprehensive intensity of the strongest historical rainstorm processes (i.e., the intensity of new rainstorm processes), the number of rainstorm days, the daily maximum precipitation, the annual precipitation, the cumulative rainfall with an annual rainfall of ≥50 mm, and the cumulative rainfall of rainstorm processes, etc., of all stations in each province (autonomous region) are statistically obtained. Then, the annual average values of each province (autonomous region) are statistically calculated, and then correlation analysis is carried out with the historical rainstorm flood affected area and direct economic losses of the province (autonomous region). The correlation coefficients are shown in Tables 5 and 6.

[0059] Table 5 Correlation coefficients between relevant rainstorm intensity indicators and rainstorm flood affected area of representative provinces (autonomous regions) in each region It can be seen from Table 5 that for the rainstorm intensity indicators calculated by the representative provinces (autonomous regions) using the usual methods, including the number of rainstorm days, the daily maximum precipitation, the annual precipitation, the cumulative rainfall with an annual rainfall of ≥50 mm, the cumulative rainfall of the original rainstorm process, etc., although most of the correlation coefficients with the rainstorm flood affected area can pass the significance level test of 0.01. Among them, for the two indicators of the cumulative rainfall with an annual rainfall of ≥50 mm and the cumulative rainfall of the original rainstorm process, except for Jiangsu and Guizhou where the correlation coefficients are equal to or lower than 0.44, the rest exceed 0.44 and pass the significance level test of 0.001. In addition, the number of rainstorm days in Ningxia and the daily maximum precipitation in Jiangsu have not passed the significance level test of 0.05. However, the correlation coefficient between the new rainstorm process intensity determined by the method of the present invention and the rainstorm flood affected area is greater than 0.5, passing the significance level test of 0.001; and except for Guizhou where it is lower than the daily maximum precipitation, it is higher than other common indicators.

[0060] After standardizing the comprehensive intensity of the rainstorm process calculated by using the method of the present invention, the "new rainstorm intensity index" is obtained. After standardizing the best existing indicator for representing rainstorm intensity, the cumulative intensity of the rainstorm process, the "original rainstorm intensity index" is obtained. Figure 4 It is a comparison chart of the historical changes of the "new rainstorm intensity index", the "original rainstorm intensity index", and the rainstorm flood affected area in Hubei Province over the years. It can be seen that their changes are basically the same. The correlation coefficients between the new and original rainstorm intensity indices and the rainstorm flood affected area are 0.80 and 0.73 respectively. The new rainstorm intensity index reflects the rainstorm flood affected area better.

[0061] Table 6 Correlation coefficients between rainstorm intensity indicators and direct economic losses of rainstorm floods in representative provinces (autonomous regions) in each region As can be seen from Table 6, the correlation coefficients between the intensity of the new rainstorm process determined by the method of the present invention and the direct economic losses caused by rainstorm floods and waterlogging, except for those in Jiangsu, Guizhou, and Guangxi provinces (autonomous regions) which passed the significance level test of 0.01; the correlation coefficients of the remaining representative provinces (autonomous regions) all passed the significance level test of 0.001, showing much better performance than the currently commonly used rainstorm intensity indicators (including the number of rainstorm days, the daily maximum precipitation, the annual precipitation, the cumulative precipitation of ≥50 mm per year, the cumulative precipitation of the original rainstorm process, etc.). Among the currently commonly used rainstorm intensity indicators, only the daily maximum precipitation (in Hebei and Guizhou) and the cumulative precipitation of ≥50 mm per year (in Guizhou) have correlation coefficients greater than that of the new rainstorm process intensity with the direct economic losses caused by rainstorm floods and waterlogging, and the performance of the remaining indicators in each representative province (autonomous region) is inferior to that of the new rainstorm process intensity. In Jiangsu, the correlation coefficients between the currently commonly used rainstorm intensity indicators and the direct economic losses caused by rainstorm floods and waterlogging are all less than 0.25 and all failed to pass the significance level test of 0.1, showing extremely poor performance.

[0062] Figure 5 It is a schematic diagram showing the comparison of the annual changes of the "new rainstorm intensity index" and the "original rainstorm intensity index" in Hubei Province over the years with the direct economic losses caused by rainstorm floods and waterlogging. It can be seen that their changes are also basically the same. The correlation coefficients between the new and original rainstorm intensity indices and the affected area of rainstorm floods and waterlogging are 0.85 and 0.76 respectively, and the new rainstorm intensity index reflects the affected area of rainstorm floods and waterlogging better.

[0063] In summary, the correlation coefficients between the intensity of the new rainstorm process in the representative provinces (autonomous regions) determined by the present invention and the affected area of rainstorm floods and waterlogging and the direct economic losses caused by rainstorm floods and waterlogging, except for those in Jiangsu, Guizhou, and Guangxi provinces (autonomous regions) which passed the significance level test of 0.01; the correlation coefficients of the remaining representative provinces (autonomous regions) all passed the significance level test of 0.001. Therefore, the overall performance of the new rainstorm process intensity in reflecting rainstorm flood disasters is much better than the currently commonly used rainstorm intensity indicators.

[0064] The above embodiments are only the main technical ideas and specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels, characterized in that: The method comprises at least the following steps when implemented: SS1. Collect topographic and environmental information of the proposed stations and daily precipitation data for the past 30 years or more, and perform quality control and preprocessing on the original data, including at least removing false values ​​exceeding the climate threshold and rigid values ​​found in the spatiotemporal consistency check, to build a long-term precipitation database with reliable quality; SS2. Based on the constructed long-term precipitation database, the maximum daily precipitation of the simulated station in previous years is counted, and the historical minimum annual maximum daily precipitation is selected as the heavy rain threshold of the simulated station. When the historical minimum annual maximum daily precipitation is less than 25 mm, 25 mm is taken as the heavy rain threshold; SS3. Based on the constructed long-term precipitation database and the determined rainstorm threshold, objectively identify all rainstorm processes in the simulated station over the years and calculate the comprehensive intensity of each rainstorm process. First, starting from the beginning of each year, take whether the daily precipitation reaches or exceeds the rainstorm threshold as the judgment condition, traverse the daily precipitation data in turn and mark all dates that reach or exceed the rainstorm threshold. Then, with each marked date as the center, slide forward and backward day by day to calculate the equivalent rainstorm intensity of the rainstorm process. The maximum equivalent rainstorm intensity generated in different combinations of days is selected as the comprehensive intensity of the rainstorm process. The period corresponding to the maximum equivalent rainstorm intensity is regarded as a complete rainstorm process and its start and end dates and process days are determined until all marked dates are traversed and all rainstorm processes are identified; SS4. Based on the objectively identified rainstorm processes in the past years and the calculated comprehensive intensity of each rainstorm process, the maximum comprehensive intensity of the rainstorm process in each year is extracted to form the annual extreme value sequence of the comprehensive intensity of the rainstorm process in the past years. The GEV function is used to fit the annual extreme value sequence, and the scale parameter, location parameter and shape parameter of the GEV function are obtained by linear moment estimation or maximum likelihood estimation; SS5. Based on the fitted GEV function, the comprehensive intensity threshold of the rainstorm process corresponding to the critical return period of flood level is calculated for 5 years, 10 years, 20 years and 50 years respectively. R 5. R 10 , R 20 , R 50 , and then the comprehensive intensity of the rainstorm process is divided into five levels according to the calculated thresholds, among which the first level of micro-impact corresponds to a rainstorm process with a comprehensive intensity less than R 5, the level 2 light impact corresponds to a rainstorm process with a comprehensive intensity greater than or equal to R 5 and less than R 10 In the case of level 3, the comprehensive intensity of the corresponding rainstorm process is greater than or equal to R 10 and less than R 20 In the case of severe impact level 4, the comprehensive intensity of the rainstorm process is greater than or equal to R 20 and less than R 50 In the case of Level 5 severe impact, the comprehensive intensity of the rainstorm process is greater than or equal to R 50 the situation; SS6. Based on the established comprehensive intensity grade standard for rainstorm processes, the comprehensive intensity of any rainstorm process at the objectively identified simulated station is graded, and at least the disaster-causing factor information including the topographic environment information, land use type, population density information and socio-economic conditions of the area where the simulated station is located is combined to comprehensively assess the disaster risk that the rainstorm process may cause in the area where the simulated station is located, and output the rainstorm disaster risk assessment results.

2. The rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels according to claim 1 is characterized in that: In step SS1, the simulated station needs to have at least 30 years of long-sequence data of daily precipitation, and the collection and preprocessing of the terrain environment and daily precipitation data of the simulated station includes at least the following sub-steps: SS1.1 Collect terrain and environmental information and daily precipitation data of the proposed station area, including at least the longitude, latitude, altitude of the proposed station and daily precipitation data for the past 30 years or more; SS1.2 Preprocess the collected daily precipitation data of the simulated station, use the climate threshold check method to eliminate false values ​​in the original data that exceed the climate threshold, perform spatiotemporal consistency checks on the daily precipitation data to identify and eliminate rigid or erroneous values, and supplement the missing precipitation data through interpolation. The climate threshold is determined by statistical analysis of the historical data of the simulated station and its surrounding meteorological stations and dynamically adjusted according to geographical and climate characteristics.

3. The rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels according to claim 2 is characterized in that: The climate threshold is determined as follows: Within 100km around the simulated station, select several long-sequence precipitation stations with similar precipitation probability distribution to the simulated station, and the correlation significance test between the selected stations and the simulated station in the past 10 years should exceed 95% confidence, and the altitude difference with the simulated station should not exceed 100m; The variance of daily precipitation of the simulated station and the selected long-series precipitation stations is calculated respectively, and the climate threshold of the simulated station is taken as three times the maximum variance of daily precipitation of all stations plus the climate average value; The raw precipitation data of the simulated stations are checked based on the determined climate thresholds, and false values ​​in the data that exceed the climate thresholds are removed.

4. The rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels according to claim 1 is characterized in that: In step SS2, when determining the rainstorm threshold of the proposed station, at least the following sub-steps are included: SS2.1 Based on the long-term precipitation database constructed in step SS1, firstly, the maximum daily precipitation of the proposed station in the past 30 years or more is counted and the annual extreme value sequence is formed; SS2.2 Sort the annual extreme value sequence composed of the maximum daily precipitation in previous years from small to large, and select the historical minimum annual maximum daily precipitation as the heavy rain threshold of the simulated station; SS2.3 Determine whether the obtained heavy rain threshold of the simulated station is less than 25 mm. If it is less than 25 mm, 25 mm will be used as the heavy rain threshold of the simulated station.

5. The method for assessing rainstorm disaster risk based on objectively identifying rainstorm processes and determining intensity levels according to claim 1, characterized in that: In step SS3, in the process of objectively identifying the rainstorm process and calculating the comprehensive intensity, at least the following sub-steps are included: SS3.1 Based on the long-term precipitation database established in step SS1 and the rainstorm threshold determined in step SS2, traverse the daily precipitation data of each year in turn, identify and mark all dates that reach or exceed the rainstorm threshold in each year, and define these dates as rainstorm days; SS3.2 takes each marked rainstorm day as the center, slides forward and backward day by day to increase the number of precipitation days until the continuous precipitation does not reach the rainstorm threshold, and calculates the equivalent rainstorm intensity of the rainstorm process after each additional day: in, Rs ( j , n ) is the j Heavy rain events lasting for n The intensity of the heavy rain was quite high. n is the number of days the rainstorm process lasts, a is the weighting coefficient and takes the value as 0.8, The number of days the rainstorm lasts is n The average precipitation amount at i =1,2,…, n , R i During the rainstorm i daily precipitation; SS3.3 For each identified potential rainstorm process, compare the equivalent rainstorm intensity under different day combinations and select the largest equivalent rainstorm intensity value as the comprehensive intensity of the rainstorm process; SS3.4 The process days corresponding to the maximum equivalent rainstorm intensity are taken as the objectively determined rainstorm process days, and the corresponding process start and end dates are taken as the start and end dates of the rainstorm process; SS3.5 Repeat steps SS3.2 to SS3.4, traverse all marked rainstorm days, identify all rainstorm processes in previous years, and calculate the start and end dates, duration and comprehensive intensity of each rainstorm process.

6. The rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels according to claim 1 is characterized in that: In step SS4, fitting the GEV function at least includes: SS4.1 Based on the comprehensive intensity of each rainstorm process in the past years obtained in step SS3, extract the maximum comprehensive intensity of the rainstorm process in each year to form the annual extreme value sequence of the comprehensive intensity of the rainstorm process in the past years. If there is no rainstorm process that meets the identification of step SS3 in a certain year, the maximum daily precipitation of that year is used as the comprehensive intensity of the rainstorm process in that year; SS4.2 uses the annual extreme value series of the comprehensive intensity of heavy rain processes to fit the GEV function, constructs a relationship model between the comprehensive intensity of heavy rain and the return period, and uses linear moment estimation or maximum likelihood estimation statistics to obtain the scale parameter, location parameter and shape parameter of the GEV function.

7. The rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels according to claim 1, characterized in that: In step SS4, before fitting the GEV function, the annual extreme value sequence is first tested for stationarity. The Mann-Kendall test or the ADF test is used to evaluate the stationarity of the annual extreme value sequence. If there is a significant trend change in the sequence, the sequence is preprocessed by a detrending method such as difference or piecewise fitting to ensure the effectiveness of the GEV function fitting.

8. The rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels according to claim 1, characterized in that: In step SS6, when comprehensively assessing the rainstorm disaster risk in combination with the disaster factor information of the area where the proposed station is located, a disaster risk assessment model is further introduced. The specific method is: adopt a disaster risk assessment framework based on the three elements of hazard, vulnerability and exposure, use the rainstorm intensity level of the proposed station as the hazard parameter, assess the regional exposure in combination with the regional population density, economic development level and land use type, and assess the regional vulnerability in combination with the terrain environment information, so as to comprehensively output the quantitative assessment results of the rainstorm disaster risk.

9. A computer program product comprising computer instructions, characterized in that The computer instructions are used to execute the rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for assessing the risk of rainstorm disasters based on objectively identifying rainstorm processes and determining intensity levels as described in any one of claims 1 to 8 is implemented.

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