A storm disaster risk assessment method based on objective identification of a storm process and determination of a strength grade, a medium and a program product

Abstract

This invention discloses a method, medium, and program product for assessing rainstorm disaster risk based on objectively identifying rainstorm processes and determining their intensity levels. Belonging to the field of risk assessment technology, the method includes: collecting and preprocessing daily precipitation data from designated stations to form a long-term precipitation series database; selecting the historical minimum annual maximum daily precipitation as a rainstorm rainfall threshold; then identifying the start and end dates, duration, and overall intensity of each rainstorm process that reaches or exceeds the threshold within each year; obtaining the overall intensity of the strongest annual rainstorm process; then constructing a rainstorm process intensity return period threshold index by fitting a GEV function to the overall intensity of the strongest rainstorm process; finally determining the rainstorm process intensity level based on the return period threshold and comprehensively assessing the rainstorm disaster risk. This invention improves the accuracy of determining rainstorm process intensity by objectively identifying rainstorm processes and determining their intensity levels.

Description

Technical Field

[0001] This invention belongs to the field of meteorological and hydrological rainstorm disaster risk assessment technology, and involves the identification of rainstorm processes, the determination of rainstorm intensity levels, and the risk analysis of rainstorm disasters. Specifically, it involves a rainstorm disaster risk assessment method, medium, and program product that uses daily precipitation observation data to identify rainstorm processes and establish a rainstorm process intensity sample sequence to classify rainstorm process intensity through typical return periods. The application of this technology can improve the objective identification of rainstorm processes and the accuracy of rainstorm disaster assessment. Background Technology

[0002] Rainstorm disasters are severe meteorological disasters that can lead to urban flooding, inundation, dam collapses, farmland inundation, and disruptions to transportation and telecommunications, causing serious harm to the social economy and people's lives and property. A key contributing factor to rainstorm disasters is the overall intensity of the rainstorm process. Accurately and reasonably identifying rainstorm processes and determining their intensity levels is not only a scientific research issue but also directly affects the accuracy of rainstorm disaster impact assessments. This, in turn, impacts the scientific prevention of rainstorm disasters and the design and operational safety of major projects, urban drainage networks, and flood control systems. Therefore, objectively identifying rainstorm processes and determining their intensity levels has significant theoretical and practical value.

[0003] Rainstorm disaster risk assessment is a crucial component of disaster prevention and mitigation efforts. Its core lies in objectively identifying rainstorm events, quantifying their intensity, and analyzing their potential impacts. Currently, rainstorm event identification generally relies on simple statistics of daily precipitation and traditional rainstorm intensity threshold standards. Specifically, existing technologies primarily identify rainstorm events based on a uniformly set daily precipitation threshold (i.e., daily precipitation greater than or equal to 50 mm) and continuous rainfall before and after it, using an artificially set 1-2 days of no precipitation as the termination standard for the rainstorm event. However, this nationally unified rainstorm threshold fails to adequately consider regional climate and topographical differences. It is too high for arid northern regions and too low for humid southern regions. Therefore, the impact of using the same rainstorm threshold standard across different regions often varies significantly. In the south, 50 mm of daily precipitation may cause minimal or almost no disaster, while in the north, especially the northwest, severe disasters may occur. Furthermore, the method of artificially setting 1 or 2 days of no precipitation to determine the termination of a rainstorm event also has flaws and shortcomings, ignoring the continuity of the precipitation process and the actual degree of impact. Therefore, the rainstorm process and intensity identified by existing technology cannot fully reflect the intensity of the rainstorm process and the degree of disaster impact.

[0004] Furthermore, classifying rainfall intensity levels is a crucial step in rainfall disaster risk assessment, directly impacting the scientific rigor of disaster prevention decisions and engineering designs. Current technologies lack a unified quantitative standard for classifying rainfall intensity levels, typically relying on experience or historical data for rough classifications. This approach fails to meet the demands for refined and scientific classification of rainfall disaster risks in practical disaster prevention and mitigation work. Especially in areas such as flood warnings, urban drainage system design, and flood control scheduling of water conservancy projects, accurate rainfall intensity level standards are essential for improving disaster prevention capabilities.

[0005] As global warming continues, extreme rainstorm events are becoming increasingly frequent, significantly impacting urban operations, transportation, and people's lives. This necessitates a higher level of precision in classifying rainstorm events and their intensity, as well as in accurately assessing their potential hazards. Therefore, objectively selecting rainstorm thresholds based on local historical climate characteristics and quantitatively determining rainstorm events and their intensity levels can effectively improve the accuracy of rainstorm event and intensity identification, thereby more accurately assessing the danger of rainstorms and providing scientific support for rainstorm prevention and disaster mitigation. This remains a pressing technical challenge. Summary of the Invention

[0006] (a) Purpose of the invention

[0007] To address the shortcomings and deficiencies in existing methods for determining the intensity of rainstorm events, including high subjectivity and low accuracy in rainstorm event identification, unscientific and unreasonable classification of rainstorm intensity levels, and the inability of rainstorm disaster risk assessment methods to accurately reflect disaster impacts, this invention aims to solve at least one of the aforementioned and other technical problems in existing technologies. The invention provides a rainstorm disaster risk assessment method, medium, and program product based on objectively identifying rainstorm events and determining their intensity levels. This method selects a heavy precipitation threshold based on local climate characteristics to determine the rainstorm threshold. Then, it objectively statistically analyzes the maximum equivalent rainstorm intensity of all rainstorm events within the year that reach or exceed the rainstorm threshold to identify the start and end dates, duration, and overall intensity of the rainstorm event. The event with the highest overall intensity within the year is selected as the strongest rainstorm event. Finally, it utilizes a generalized extreme value distribution (GSD) to further analyze the intensity. By fitting the probability density function of the comprehensive intensity of the strongest rainstorm process (GEV), a return period threshold index for the intensity of the rainstorm process is constructed. Finally, the comprehensive intensity level of the rainstorm process is determined based on the return period threshold index value, so as to objectively determine the rainstorm process and intensity level, improve the accuracy of determining the intensity of the rainstorm process, enhance the objectivity and accuracy of rainstorm disaster risk assessment, and provide a scientific basis for flood prevention and disaster reduction.

[0008] (II) Technical Solution

[0009] To achieve the objective of this invention and solve its technical problems, the present invention adopts the following technical solution:

[0010] The first objective of this invention is to provide a method for assessing the risk of rainstorm disasters based on objectively identifying rainstorm processes and determining their intensity levels. This method is used to objectively identify rainstorm processes based on meteorological observation data, quantify the comprehensive intensity of rainstorms, classify intensity levels, and conduct a more accurate assessment of rainstorm disaster risks. This aims to improve the early warning and prevention capabilities for rainstorm disasters and provide a scientific basis for mitigating rainstorm disaster losses. The method, when implemented, includes at least the following steps:

[0011] SS1. Collection and preprocessing of topographic environment and daily precipitation data for the proposed station.

[0012] Collect topographic and environmental information of the proposed station and daily precipitation data for the past 30 years or more. Perform quality control and preprocessing on the raw data to remove false values ​​that exceed the climate threshold and dead values ​​found in the spatiotemporal consistency check, and establish a reliable long-term precipitation series database.

[0013] SS2. Determine the rainstorm threshold for the proposed station using the annual daily maximum precipitation sequence.

[0014] Based on the long-term precipitation database established in step SS1, the maximum daily precipitation of the proposed station is statistically analyzed. The maximum daily precipitation of the minimum year in history is selected as the rainstorm threshold of the proposed station. When the maximum daily precipitation of the minimum year in history is less than 25 mm, 25 mm is taken as the rainstorm threshold of the proposed station.

[0015] SS3. Objectively identify all rainfall events at the proposed monitoring station and calculate the overall intensity.

[0016] Based on the long-term precipitation database established in step SS1 and the rainstorm threshold determined in step SS2, all rainstorm events of the proposed station throughout the year are objectively identified and the comprehensive intensity of each rainstorm event is calculated. Starting from the beginning of each year, the daily precipitation data is iterated through and all dates that reach or exceed the rainstorm threshold are marked, using each marked date as the center and sliding forward and backward day by day to calculate the equivalent rainstorm intensity of the rainstorm event. The maximum equivalent rainstorm intensity generated in different combinations of days is selected as the comprehensive intensity of the rainstorm event. The time period corresponding to the maximum equivalent rainstorm intensity is taken as a complete rainstorm event and its start and end dates and the number of days are determined, until all marked dates are traversed and all rainstorm events are identified.

[0017] SS4. Establish the annual extreme value sequence of the comprehensive intensity of rainstorm processes at the proposed stations and fit the extreme value distribution function.

[0018] Based on all the rainstorm events of the simulation station in each year and the comprehensive intensity of each rainstorm event calculated in step SS3, the comprehensive intensity of the largest rainstorm event in each year is extracted to form the annual extreme value sequence of the comprehensive intensity of rainstorm events. 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 using linear moment estimation or maximum likelihood estimation.

[0019] SS5. Standard for classifying the comprehensive intensity level of rainstorm processes based on the critical return period of flood levels.

[0020] Based on the generalized extreme value distribution function fitted in step SS4, the comprehensive intensity thresholds of the rainstorm process corresponding to the critical return periods of 5 years, 10 years, 20 years, and 50 years for flood level are calculated respectively. R 5. R 10 , R 20 , R 50 Based on the calculated thresholds, the overall intensity of the rainstorm process is divided into five levels, with Level 1 (micro-impact) corresponding to an overall intensity of less than [missing information]. R In scenario 5, a level 2 light impact corresponds to a comprehensive intensity of the rainstorm process greater than or equal to... R 5 and less than R 10 In this scenario, the overall intensity of the rainstorm process corresponding to Level 3 is greater than or equal to... R 10 And less than R 20 In this situation, a level 4 severe impact corresponds to a comprehensive intensity of rainstorm events greater than or equal to [a certain value]. R 20 And less than R 50 In this situation, the comprehensive intensity of the rainstorm process corresponding to a Level 5 extremely severe impact is greater than or equal to... R 50 The situation;

[0021] SS6. Risk Assessment of Rainstorm Disasters in the Area Where the Proposed Station is Located

[0022] Based on the comprehensive intensity level standard of rainstorm process established in step SS5, the comprehensive intensity of any rainstorm process at the objectively identified calculation station is classified into levels. 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 calculation station is located, is combined to comprehensively assess the disaster risk that the rainstorm process may cause in the area where the calculation station is located, and output the rainstorm disaster risk assessment results.

[0023] The second objective of this invention is to provide a computer program product, including computer instructions, which are used to execute the above-mentioned rainstorm disaster risk assessment method based on objectively identifying rainstorm processes and determining intensity levels.

[0024] The third objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for assessing rainstorm disaster risk based on objectively identifying rainstorm processes and determining intensity levels.

[0025] (III) Technical Effects

[0026] Compared with the prior art, the rainstorm disaster risk assessment method, medium, and program products of the present invention based on objectively identifying rainstorm processes and determining intensity levels have the following beneficial and significant technical effects:

[0027] When determining the rainfall process and intensity level of a proposed monitoring station, this invention first determines its "rainstorm threshold" based on the station's climatic characteristics. Then, it objectively statistically analyzes the maximum equivalent rainfall intensity of precipitation processes that reach or exceed the "rainstorm threshold" to identify the start and end dates, duration, and overall intensity of the rainfall process. Next, it selects the overall intensity of the strongest rainfall process in history and constructs a return period threshold index for the overall intensity of the rainfall process using a generalized extreme value distribution function. Finally, it determines the intensity level of the rainfall process based on the return period threshold index. This solves the problems of traditional methods, such as the "rainstorm threshold" being unrelated to local climate and the inaccurate intervals between rainfall events. It ensures the objectivity of determining the rainfall threshold and the rainfall process, and the intensity of the rainfall process reflects the combined effect of the rainfall amount and duration. The determined intensity level has a higher correlation with the affected area and economic losses from rainstorm floods than existing methods and has passed the significance level test (…). α =0.01), which can more accurately reflect the impact of rainstorm disasters. Attached Figure Description

[0028] Figure 1 The diagram shows the implementation process of the rainstorm disaster risk assessment method based on objective identification of rainstorm processes and determination of intensity levels according to the present invention.

[0029] Figure 2 The diagram shows the daily precipitation of a rainstorm process at Wuhan Station as objectively identified by this invention, and the statistically calculated equivalent rainstorm intensity of different combinations of days including the maximum daily precipitation.

[0030] Figure 3 The figure shows the changes in the comprehensive intensity (new rainstorm intensity index) of rainstorm events over the years and the maximum daily precipitation over the years at Wuhan Station, calculated using the method of the present invention.

[0031] Figure 4The diagram shows a comparison of the annual changes in the comprehensive intensity (new rainstorm intensity index) and cumulative intensity (original rainstorm intensity index) of rainstorm processes in Hubei Province, as well as the area affected by rainstorms and floods, calculated using the method of this invention.

[0032] Figure 5 The diagram shows a comparison of the standardized historical variations of the comprehensive intensity (new rainstorm intensity index) and cumulative intensity (original rainstorm intensity index) of rainstorm processes in Hubei Province, as well as the direct economic losses from rainstorms and floods, calculated using the method of this invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. The described embodiments are some, but not all, embodiments of this invention, and are exemplary and intended to explain the invention, not to limit it. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] This invention aims to provide a method for assessing the risk of rainstorm disasters based on objectively identifying rainstorm processes and determining their intensity levels. This method is used to objectively identify rainstorm processes based on meteorological observation data, quantify the comprehensive intensity of rainstorms and classify their intensity levels, and conduct a more accurate assessment of the risk of rainstorm disasters. This will improve the ability to warn and prevent rainstorm disasters and provide a scientific basis for mitigating rainstorm disaster losses.

[0035] Example 1

[0036] As a specific example, such as Figure 1 As shown, the rainstorm disaster risk assessment method of the present invention, based on objective identification of rainstorm processes and determination of intensity levels, first collects and preprocesses precipitation data from the proposed calculation stations to determine rainstorm thresholds, objectively identifies all rainstorm processes at the proposed calculation stations and determines their comprehensive intensity, establishes an annual extreme value sequence of comprehensive intensity of rainstorm processes, classifies rainstorm intensity levels according to the comprehensive intensity threshold of the return period, and finally conducts a rainstorm disaster risk assessment. Specifically, the method includes at least the following steps in its implementation:

[0037] SS1. Collection and preprocessing of topographic environment and daily precipitation data for the proposed station.

[0038] We collected topographic and environmental information of the proposed station and daily precipitation data for the past 30 years or more. We also performed quality control and preprocessing on the raw data, removing false values ​​that exceeded the climate threshold and dead values ​​found in the spatiotemporal consistency check, and established a reliable long-term precipitation series database.

[0039] SS2. Determine the rainstorm threshold for the proposed station using the annual daily maximum precipitation sequence.

[0040] Based on the long-term precipitation database established in step SS1, the historical maximum daily precipitation of the proposed station is statistically analyzed, and the historical minimum annual maximum daily precipitation is selected as the rainstorm threshold of the proposed station. When the historical minimum annual maximum daily precipitation is less than 25 mm, 25 mm is taken as the rainstorm threshold of the proposed station.

[0041] SS3. Objectively identify all rainfall events at the proposed monitoring station and calculate the overall intensity.

[0042] Based on the long-term precipitation database established in step SS1 and the rainstorm threshold determined in step SS2, all rainstorm events of the proposed station throughout the year are objectively identified and the comprehensive intensity of each rainstorm event is calculated. Starting from the beginning of each year, the daily precipitation data is iterated through and all dates that reach or exceed the rainstorm threshold are marked, using each marked date as the center and sliding forward and backward day by day to calculate the equivalent rainstorm intensity of the rainstorm event. The maximum equivalent rainstorm intensity generated in different combinations of days is selected as the comprehensive intensity of the rainstorm event. The time period corresponding to the maximum equivalent rainstorm intensity is taken as a complete rainstorm event and its start and end dates and the number of days are determined, until all marked dates are traversed and all rainstorm events are identified.

[0043] SS4. Establish the annual extreme value sequence of the comprehensive intensity of rainstorm processes at the proposed stations and fit the extreme value distribution function.

[0044] Based on all the rainstorm events of the simulation station in each year and the comprehensive intensity of each rainstorm event calculated in step SS3, the comprehensive intensity of the largest rainstorm event in each year is extracted to form the annual extreme value sequence of the comprehensive intensity of rainstorm events. The generalized extreme value (GEV) distribution function is used to fit the annual extreme value sequence, and the scale parameter, location parameter and shape parameter of the GEV are obtained by using the linear moment estimation method.

[0045] SS5. Standard for classifying the comprehensive intensity level of rainstorm processes based on the critical return period of flood levels.

[0046] Based on the generalized extreme value distribution function fitted in step SS4, the comprehensive intensity thresholds of the rainstorm process corresponding to the critical return periods of 5 years, 10 years, 20 years, and 50 years for flood level are calculated respectively. R 5. R 10 , R 20 , R 50Based on the calculated thresholds, the overall intensity of the rainstorm process is divided into five levels, with Level 1 (micro-impact) corresponding to an overall intensity of less than [missing information]. R In scenario 5, a level 2 light impact corresponds to a comprehensive intensity of the rainstorm process greater than or equal to... R 5 and less than R 10 In this scenario, the overall intensity of the rainstorm process corresponding to Level 3 is greater than or equal to... R 10 And less than R 20 In this situation, a level 4 severe impact corresponds to a comprehensive intensity of rainstorm events greater than or equal to [a certain value]. R 20 And less than R 50 In this situation, the comprehensive intensity of the rainstorm process corresponding to a Level 5 extremely severe impact is greater than or equal to... R 50 The following table shows the classification of the comprehensive intensity level and impact degree of the rainstorm process.

[0047] Table 1. Classification of Comprehensive Intensity Levels and Impact Degrees of Rainstorm Processes

[0048]

[0049] SS6. Risk Assessment of Rainstorm Disasters in the Area Where the Proposed Station is Located

[0050] Based on the comprehensive intensity level standard of rainstorm process established in step SS5, the comprehensive intensity of any rainstorm process at the objectively identified calculation station is classified into levels. 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 calculation station is located, is combined to comprehensively assess the disaster risk that the rainstorm process may cause in the area where the calculation station is located, and output the rainstorm disaster risk assessment results.

[0051] Example 2

[0052] Based on the above 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 topographic environment and daily precipitation data of the simulation station.

[0053] SS1.1 Collect topographic environment and daily precipitation data of the proposed station, including at least: the longitude, latitude, altitude of the proposed station, the daily precipitation during the required period of the rainstorm process, and daily precipitation observation data for the past 30 years or more.

[0054] SS1.2 preprocesses the collected meteorological observation data from the proposed stations. First, it removes spurious values ​​exceeding the climate threshold from the raw data using climate thresholds for meteorological variables. For static values ​​that remain unchanged across multiple consecutive observation periods, it further... Spatiotemporal consistency checks are performed in a manner that filters out and removes dead values. u t Represents a specific moment in meteorological observation data. t The observed values, u t+1 express t The observation value at time +1. Meanwhile, missing precipitation data were filled in using interpolation methods. The climate threshold was determined through statistical analysis of historical data from the proposed station and surrounding meteorological stations, and dynamically adjusted based on geographical and climatic characteristics.

[0055] SS1.3 Standardization and Database Construction: Based on the above-processed precipitation data, the algorithm formula is used to standardize the data. Standardization is performed to ensure the data is dimensionless, facilitating subsequent analysis. X The original data, The mean of the data. σ Standard deviation X' The data is standardized. The final product is a reliable, high-quality, standardized long-term precipitation database with no missing values ​​or spurious values.

[0056] As a preferred method, in sub-step SS1.2, the climate threshold is determined as follows: First, within a 100km radius of the proposed station, several long-sequence precipitation stations with similar precipitation probability distributions to the proposed station are selected. The selected stations must have a correlation significance test with the proposed station's precipitation over the past 10 years exceeding 95% confidence, and their altitude difference from the proposed station must not exceed 100m. Second, the variance of daily precipitation for the proposed station and each selected long-sequence precipitation station is calculated. The maximum variance of daily precipitation for all stations is taken as three times the maximum variance plus the climate average as the climate threshold for the proposed station. Then, based on the determined climate threshold, the original precipitation data of the proposed station is checked, and spurious values ​​exceeding the climate threshold are removed.

[0057] Example 3

[0058] Based on the above embodiments 1 and 2, this embodiment 3 further describes in detail the specific operation steps for determining the "rainstorm threshold" of the proposed station in step SS2, and at the same time, combines the statistical sample sequence to rationally set the rainstorm threshold to ensure the scientific nature and regional applicability of the threshold.

[0059] SS2.1 Based on the meteorological observation data preprocessed in step SS1, daily precipitation data of the proposed station length sequence are used to statistically establish a sample sequence of the maximum daily precipitation of the proposed station over the past 30 years or more.

[0060] SS2.2 sorts the obtained historical daily maximum precipitation sample sequences in ascending order, marks the minimum value of the sample sequences, and selects the minimum value in the daily maximum precipitation sequence as the "rainstorm threshold" (also known as the "heavy precipitation threshold") for the proposed station. Z v express.

[0061] SS2.3 Determine whether the obtained rainfall threshold for the proposed station is less than 25mm. If the rainfall threshold is greater than or equal to 25mm, directly use the minimum value as the rainfall threshold for the proposed station. If the rainfall threshold is less than 25mm, then use the rainfall threshold for the proposed station. Z v =25mm.

[0062] Following the above method, a historical daily maximum precipitation sample sequence is established, yielding the minimum and maximum values ​​for each station's historical daily maximum precipitation sample sequence. Table 2 provides an example of the rainstorm thresholds for seven representative stations in several regions of China. Only the minimum annual daily maximum precipitation at Yinchuan station is 11.9 mm, less than 25 mm; therefore, its rainstorm threshold is [value missing]. Z v =25mm. The minimum annual daily maximum precipitation of other representative stations is greater than 25mm, so the minimum value can be directly taken as the "rainstorm threshold".

[0063] Table 2. Historical maximum daily precipitation sequence and rainstorm threshold for representative stations in each region.

[0064]

[0065] Through the above operations, this embodiment 3 refines and supplements the calculation and determination of the rainstorm threshold, ensuring that the threshold can objectively and scientifically reflect the rainstorm characteristics of each representative station, and effectively connect with the subsequent rainstorm process identification and intensity assessment steps.

[0066] Example 4

[0067] Based on the above embodiments 1 to 3, this embodiment 4 provides a detailed description of the steps in step SS3 for objectively identifying all rainstorm processes and statistically analyzing their comprehensive intensity at the proposed station.

[0068] SS3.1 Based on the meteorological observation data preprocessed in step SS1, the rainstorm threshold obtained in step SS2 is then used. Z vFirst, it iterate through the daily precipitation data for each year, identifying and marking all rainfall events that reach or exceed the rainstorm threshold within each year. Z v The dates were determined and these dates were defined as rainstorm days;

[0069] SS3.2 Using each marked rainstorm day as the center, the number of precipitation days is increased day by day, moving forward and backward, until the consecutive precipitation amounts no longer reach the rainstorm threshold, at which point the process terminates. The equivalent rainstorm intensity is then calculated for each additional day. Rs Its calculation formula is:

[0070]

[0071] In the formula: Rs ( j , n ) is the first j The number of rainstorm events and their duration were: n The intensity of the rainstorm was comparable to that of the time; n This refers to the number of days in the heavy rain event, expressed in days. a This is the weighting coefficient, with a value of 0.8; The number of days during the rainstorm process is n The average precipitation at that time, and , R i The first during the rainstorm i Rainfall of the day, i =1,2,…, n .

[0072] SS3.3 For each identified potential rainstorm event, the maximum value of the equivalent rainstorm intensity over different rainstorm event durations is taken as the comprehensive intensity of the rainstorm event. Rm and record the corresponding Rm The number of days is the number of days of the rainstorm process. Rd Its calculation formula is:

[0073]

[0074] In the formula: To identify different combinations of time periods within a rainstorm event through comparison (i.e., n =1,2,…, m The maximum value of ) m The maximum value equivalent to a heavy rainstorm. Rm The corresponding number of days.

[0075] SS3.4 The maximum value of the equivalent rainstorm intensity obtained from the above comparison. Rm The overall intensity of this rainstorm event, and the number of days corresponding to it. m That is, the objectively determined number of days of the rainstorm process. Rd Record the maximum value at the same time. Rm The corresponding start and end dates of the process are taken as the start and end dates of the rainstorm process.

[0076] SS3.5 Repeat steps SS3.2 to SS3.4 to iterate through all marked rainstorm days, identify all rainstorm events throughout the year, and calculate the start and end dates, duration, and overall intensity of each rainstorm event.

[0077] Following the steps above, taking the daily precipitation data of Wuhan Station in 2022 as an example, and combining it with the established rainstorm threshold... Zv First, objectively identify the 2022 rainstorm process at Wuhan Station (such as...). Figure 2 (The intensity of the heavy rainfall during the heavy rainfall process) Rs The calculation formula for various combinations is quite similar to the process of calculating rainstorm intensity, and is used through the formula. By determining the maximum intensity of the equivalent rainstorm and the corresponding start and end dates, the first rainstorm event was identified as lasting 10 days, from March 16th to March 25th, with a comprehensive intensity... Rm The rainfall was 124.4 mm; the second rainstorm occurred from April 22nd to April 28th, lasting 7 days, with a comprehensive intensity of [missing information]. Rm The rainfall was 113.3 mm; the third rainstorm occurred from June 19th to June 28th, lasting 10 days, with a comprehensive intensity of [missing information]. Rm The rainfall was 120.3 mm; the fourth rainstorm occurred from July 17th to July 21st, lasting 5 days, with a comprehensive intensity of 120.3 mm. Rm It is 117.1mm.

[0078] Figure 2 This refers to the daily precipitation at Wuhan station from June 18th to June 29th, 2022, and the calculated equivalent rainfall intensity for different combinations of days. (Using the formula...) The equivalent rainfall intensity for different combinations of precipitation matrices with different number of days was calculated. The red bars in the figure represent the equivalent rainfall intensity for different combinations of number of days, and the blue line marked with an arrow represents the equivalent rainfall intensity for the combination including the central day of June 28th with a precipitation of 99.8 mm. The 120.3 mm figure is calculated using the formula... The maximum value selected is the overall intensity of the rainstorm. Rm The corresponding period from June 19 to June 28 is the time of this rainstorm process, which is the objectively identified third rainstorm process in 2022, and also the rainstorm process with the strongest comprehensive intensity among the four rainstorm processes in 2022.

[0079] Example 5

[0080] Based on the above embodiments 1 to 4, this embodiment 5 further describes in detail the specific method and calculation process of establishing the annual extreme value sequence of the comprehensive intensity of the rainstorm process of the simulation station and fitting the generalized extreme value distribution GEV in step SS4 of the present invention.

[0081] Extraction and Construction of Annual Extreme Value Sequences in SS4.1

[0082] Based on the comprehensive intensity of each annual rainstorm event obtained in step SS3, the comprehensive intensity of the largest rainstorm event within the year is selected to form an annual extreme value sequence of comprehensive rainstorm event intensities:

[0083]

[0084] in, Rm ( i ) is the first i The overall intensity of the strongest rainstorm of the year n To ensure sufficient time span, the annual extreme value sequence is at least 30 years long, meeting the sample requirements for extreme value distribution statistics. If no rainstorm process meeting the criteria is identified in a given year, the day with the highest daily precipitation in that year is selected as the representative rainstorm process, and the comprehensive intensity is directly taken from the precipitation value of that day.

[0085] SS4.2 Fitting the Generalized Extreme Value Distribution (GEV)

[0086] In this embodiment of the invention, the fitted extreme value distribution function refers to the generalized extreme value distribution (GEV) that is fitted using the annual extreme value sequence of the comprehensive intensity of the rainstorm process, based on the theory of extreme value distribution.

[0087] Assuming an annual extreme value sequence of the comprehensive intensity of rainstorm events X 1, X 2, X 3, ..., X m If is an independent random variable following a GEV distribution, then its cumulative distribution function is... F ( X ), i.e., variables X < x The probability is:

[0088] when k ≠0, then ,and k When >0, -∞< x ≤ u + a / k ; k When <0, u + a / k ≤ x <+ ∞.

[0089] when k =0 and - ∞ < x < ∞ ,

[0090] In the formula: k , u , a These are shape parameters, position parameters, and scale parameters, respectively. x It is a certain threshold (such as the overall intensity of a rainstorm or the maximum daily precipitation).

[0091] SS4.3 Linear Moment Estimation Method for Parameter Calculation

[0092] GEV's shape parameters, position parameters, and scale parameters k , u , a It can be calculated using the linear moment estimation method based on a statistical sample. The specific calculation steps are as follows:

[0093] (1) The probability weight moments are defined as follows:

[0094]

[0095] In the formula: i , j , k For real numbers, when j = k =0 and l When it is a non-negative integer, M l,0,0 The order origin moment of the normal law is defined as follows: M (k) = M 1,0,k It is a regular probability weighted rectangle.

[0096] Arrange the sample sequences in ascending order. x 1≤ x 2≤…≤ x n , M (k) The unbiased estimator is:

[0097]

[0098] (2) The linear moment is a linear combination of the probability weight moments, expressed as follows:

[0099]

[0100] …………

[0101] Define ratio These are L-variance (L-Cv), L-skewness (L-skewness), and L-kurtosis (L-kurtosis).

[0102] (3) The linear moment estimate of the GEV distribution function parameter is calculated by the following formula:

[0103]

[0104]

[0105]

[0106] In the formula: k , u , a These are the shape parameter, position parameter, and scale parameter, respectively.

[0107] Following the method described above, we obtain the following: Figure 3 The annual extreme values ​​of daily maximum precipitation and comprehensive intensity of rainstorm events at Wuhan Station are shown. Based on the linear moment estimation method of this sequence, the three parameters of the GEV distribution function at Wuhan Station are obtained. a , u , k The rainfall amounts were 52.31, 129.40, and -0.18, respectively. The strongest rainstorm event recorded at Wuhan Station occurred from June 19th to July 6th, 2016, lasting 18 days and involving four major rainstorms: 180.0 mm on June 19th, 162.8 mm on July 1st, 153.1 mm on July 2nd, and 241.5 mm on July 6th; the total intensity was 480.4 mm. The highest daily rainfall recorded was 298.5 mm on June 20th, 1982.

[0108] Based on the above, Example 5 comprehensively refined step SS4, completed the establishment of the annual extreme value sequence and the fitting of the GEV distribution, and provided a scientific basis for rainstorm disaster risk assessment.

[0109] Example 6

[0110] Based on the above embodiments 1 to 5, this embodiment 6 further describes in detail the method of classifying the comprehensive intensity level standard of the corresponding rainstorm process according to the critical return period of the flood level in step SS5, and verifies and explains it in combination with actual calculation results.

[0111] 5.1 Basis for classifying the critical return period of flood levels

[0112] Based on the standard flood element return periods in the "Hydrological Information Forecasting Specification" (GB / T 22482-2008): less than 5 years is considered a general flood; 5 years or more but less than 20 years is considered a relatively large flood; 20 years or more but less than 50 years is considered a major flood; and 50 years or more is considered an extremely large flood. The return periods for key disaster prevention parameters include: less than 10 years, 10-20 years, 20-50 years, and more than 50 years. Therefore, the comprehensive intensity of rainstorm events is divided into five levels based on their return periods: less than 5 years is considered a minor impact; 5 years or more but less than 10 years is considered a minor impact; 10 years or more but less than 20 years is considered a moderate impact; 20 years or more but less than 50 years is considered a major impact; and 50 years or more is considered an extremely large impact (see Table 1).

[0113] 5.2 Calculation of the return period of the comprehensive intensity of the rainstorm process

[0114] Based on the three parameters (shape parameter) of the generalized extreme value distribution of the rainstorm intensity obtained in step SS4 k Position parameters u Scale parameters a It can calculate the key return periods of the comprehensive intensity of a rainstorm process. T =5 a 10 a 20 a 50 a The corresponding comprehensive strength value R ( T The formula is as follows:

[0115] when k ≠0 o'clock,

[0116] when k When =0, .

[0117] 5.3 Example Verification

[0118] Using the above method, the three parameters of the GEV distribution of the comprehensive intensity of the rainstorm process were obtained, and the comprehensive intensity of the strongest rainstorm process in each region and the key return periods were calculated for representative stations in each region. T =5, 10, 20, 50 a The corresponding comprehensive intensity threshold R ( T See Table 3. For example, the key return periods for each station at Wuhan Station (…). T The comprehensive strength thresholds corresponding to =5, 10, 20, and 50a) are respectively R 5 = 219.5mm R 10 =274.6mm R 20 =335.0mm R50 =425.8mm; the strongest combined intensity of rainstorms in history is 480.4mm.

[0119] Table 3. Historical Comprehensive Intensity of the Strongest Rainstorms and Comprehensive Intensity Thresholds for Key Return Periods at Representative Regional Stations

[0120]

[0121] Through the above content, this embodiment 6 provides a detailed explanation and example verification of step SS5, systematically completes the comprehensive intensity level classification of rainstorm processes, and provides data support and methodological guidance for subsequent rainstorm disaster risk assessment.

[0122] Example 7

[0123] Based on the above embodiments 1 to 6, this embodiment 7 describes in detail the method for classifying the intensity of the rainstorm process of the proposed station in step SS6, and provides an example of level assessment in conjunction with the actual rainstorm process of Wuhan station in 2016.

[0124] Following the above method, based on the objective identification in step SS2, the four rainstorm events and their comprehensive intensities at Wuhan station in 2016 can be obtained. Z As shown in Table 4, the intensity thresholds for each recurrence period (multi-year return period) of rainstorm events in Table 3 are then determined. R 5. R 10 , R 20 , R 50 Substituting these values ​​into Table 1, we can determine the level and impact of the four rainstorm events in 2016 (see Table 4).

[0125] Table 4. Assessment of the Level and Impact of Four Rainstorm Events at Wuhan Station in 2016

[0126]

[0127] Through this embodiment 7, the system completed the classification of the intensity of the rainstorm process and the impact assessment, which further verified the results of embodiment 6 and laid the data foundation for regional risk analysis and emergency management of rainstorm disasters.

[0128] Example 8

[0129] Based on the above embodiments 1 to 7, this embodiment 8 further utilizes the above-mentioned objective identification of rainstorm processes and determination of comprehensive intensity of the present invention to conduct correlation analysis and effect verification of the comprehensive intensity of rainstorm processes over the years with rainstorm flood disasters and direct economic losses.

[0130] Representative provinces (regions) from different regions across the country were selected: Liaoning in Northeast China, Hebei in North China, Ningxia in Northwest China, Jiangsu in East China, Hubei in Central China, Guizhou in Southwest China, and Guangxi in South China. The comprehensive intensity of the strongest rainstorm process (i.e., the intensity of the new rainstorm process) and existing rainstorm intensity indicators such as the number of rainstorm days, maximum daily precipitation, annual precipitation, cumulative annual rainfall ≥50mm, and cumulative rainfall of the rainstorm process were statistically analyzed for all stations in each province (region). Then, the historical average values ​​of each province (region) were statistically analyzed, and correlation analysis was performed with the historical rainstorm and flood disaster area and direct economic losses of the province (region). The correlation coefficients are shown in Tables 5 and 6.

[0131] Table 5. Correlation coefficients between relevant rainstorm intensity indicators and rainstorm flood-affected areas in representative provinces (regions) of each region.

[0132]

[0133] Table 5 shows that the correlation coefficients between the rainstorm intensity indicators calculated using conventional methods for representative provinces (regions), including the number of rainstorm days, maximum daily precipitation, annual precipitation, cumulative annual rainfall ≥50mm, and cumulative rainfall of the original rainstorm process, and the area affected by rainstorms and floods, mostly passed the 0.01 significance level test. Specifically, the correlation coefficients for cumulative annual rainfall ≥50mm and cumulative rainfall of the original rainstorm process, except for Jiangsu and Guizhou where the correlation coefficients were equal to or lower than 0.44, all exceeded 0.44, passing the 0.001 significance level test. However, the number of rainstorm days in Ningxia and the maximum daily precipitation in Jiangsu did not pass the 0.05 significance level test. But the correlation coefficients between the intensity of the new rainstorm process determined by the method of this invention and the area affected by rainstorms and floods were all greater than 0.5, passing the 0.001 significance level test; and except for Guizhou where the correlation coefficient was lower than the maximum daily precipitation, all were higher than other commonly used indicators.

[0134] The "new rainstorm intensity index" is obtained by standardizing the comprehensive intensity of the rainstorm process calculated using the method of this invention, and the "original rainstorm intensity index" is obtained by standardizing the cumulative intensity of the rainstorm process, which is the best existing indicator of rainstorm intensity. Figure 4 This is a comparison chart of the annual changes in the "new rainstorm intensity index," "original rainstorm intensity index," and the area affected by rainstorms and floods in Hubei Province. It can be seen that their changes are basically consistent. The correlation coefficients between the new and original rainstorm intensity indices and the area affected by rainstorms and floods are 0.80 and 0.73, respectively, indicating that the new rainstorm intensity index reflects the area affected by rainstorms and floods better.

[0135] Table 6. Correlation coefficients between rainfall intensity indices and direct economic losses from rainstorms and floods in representative provinces (regions) of each region.

[0136]

[0137] As shown in Table 6, the correlation coefficients between the intensity of new rainstorm events and the direct economic losses from rainstorms and floods determined by the method of this invention, except for Jiangsu, Guizhou, and Guangxi provinces (regions) where the correlation coefficients passed the 0.01 significance level test, all other representative provinces (regions) showed correlation coefficients that passed the 0.001 significance level test. This performance is significantly better than commonly used rainstorm intensity indicators (including the number of rainstorm days, maximum daily precipitation, annual precipitation, cumulative annual rainfall ≥50mm, and cumulative rainfall from the original rainstorm event). Currently used rainstorm intensity indicators only show higher correlation coefficients between maximum daily precipitation (Hebei and Guizhou) and cumulative annual rainfall ≥50mm (Guizhou) than the intensity of new rainstorm events; the other indicators perform worse in each representative province (region). In Jiangsu, the correlation coefficients between currently used rainstorm intensity indicators and the direct economic losses from rainstorms and floods are all below 0.25 and have not passed the 0.1 significance level test, indicating extremely poor performance.

[0138] Figure 5 This is a comparative diagram showing the historical changes of the "new rainstorm intensity index," "original rainstorm intensity index," and direct economic losses from rainstorms and floods in Hubei Province. It can be seen that their changes are basically consistent. The correlation coefficients between the new and original rainstorm intensity indices and the affected area from rainstorms and floods are 0.85 and 0.76, respectively, indicating that the new rainstorm intensity index reflects the affected area from rainstorms and floods more effectively.

[0139] In summary, the correlation coefficients between the intensity of new rainstorm events and the affected area and direct economic losses of rainstorms and floods in representative provinces (regions) determined by this invention passed the 0.001 significance level test for Jiangsu, Guizhou, and Guangxi provinces (regions). Therefore, the correlation coefficients of the new rainstorm events are generally much better than the rainstorm intensity indicators commonly used now in reflecting rainstorm and flood disasters.

[0140] The above embodiments are merely the main technical ideas and specific implementations of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the risk of rainstorm disasters based on objectively identifying rainstorm processes and determining their intensity levels, characterized in that, The method, when implemented, includes at least the following steps: SS1. Collect topographic and environmental information of the proposed station and daily precipitation data for the past 30 years or more, and perform quality control and preprocessing on the raw data, including at least removing spurious values ​​that exceed the climate threshold and dead values ​​found in the spatiotemporal consistency check, and construct a reliable long-term precipitation database. SS2. Based on the constructed long-term precipitation database, the maximum daily precipitation of the proposed station is statistically analyzed. The maximum daily precipitation of the minimum historical year is selected as the rainstorm threshold of the proposed station. When the maximum daily precipitation of the minimum historical year is less than 25 mm, 25 mm is taken as the rainstorm threshold. SS3. Based on the constructed long-term precipitation database and the determined rainstorm threshold, objectively identify all rainstorm events of the proposed station throughout the year and calculate the comprehensive intensity of each rainstorm event. First, starting from the beginning of each year, use whether the daily precipitation reaches or exceeds the rainstorm threshold as the judgment condition, and sequentially traverse the daily precipitation data 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 event. Select the maximum equivalent rainstorm intensity generated in different combinations of days as the comprehensive intensity of the rainstorm event. Take the period corresponding to the maximum equivalent rainstorm intensity as a complete rainstorm event and determine its start and end dates and the number of days of the event, until all marked dates are traversed and all rainstorm events are identified. SS4. Based on the objectively identified simulation stations and all annual rainstorm events and the calculated comprehensive intensity of each rainstorm event, the comprehensive intensity of the largest rainstorm event in each year is extracted to form the annual extreme value sequence of the comprehensive intensity of rainstorm events. 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 using linear moment estimation or maximum likelihood estimation. SS5. Based on the fitted GEV function, calculate the comprehensive intensity threshold of the rainstorm process corresponding to the critical return periods of 5 years, 10 years, 20 years and 50 years for flood level. R 5. R 10 , R 20 , R 50 Based on the calculated thresholds, the overall intensity of the rainstorm process is divided into five levels, with Level 1 (micro-impact) corresponding to an overall intensity of less than [missing information]. R In scenario 5, a level 2 light impact corresponds to a comprehensive intensity of the rainstorm process greater than or equal to... R 5 and less than R 10 In this scenario, the overall intensity of the rainstorm process corresponding to Level 3 is greater than or equal to... R 10 And less than R 20 In this situation, a level 4 severe impact corresponds to a comprehensive intensity of rainstorm events greater than or equal to [a certain value]. R 20 And less than R 50 In this situation, the comprehensive intensity of the rainstorm process corresponding to a Level 5 extremely severe impact is greater than or equal to... R 50 The situation; SS6. Based on the established comprehensive intensity level standard for rainstorm processes, classify the comprehensive intensity of any rainstorm process at the objectively identified calculation station, and comprehensively assess the disaster risk that the rainstorm process may cause in the area where the calculation station is located, taking into account at least the disaster-causing factors information including the topographic environment information, land use type, population density information and socio-economic conditions of the area where the calculation station is located, and output the rainstorm disaster risk assessment results.

2. 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 SS1, the proposed station must have at least 30 years of long-term daily precipitation data, and the collection and preprocessing of the proposed station's topographic environment and daily precipitation data must include at least the following sub-steps: SS1.1 Collect topographic and environmental information and daily precipitation data of the area where the proposed station is located, including at least the longitude, latitude, altitude of the proposed station and daily precipitation data for the past 30 years or more; SS1.2 preprocesses the collected daily precipitation data of the proposed station, using a climate threshold check method to remove spurious values ​​exceeding the climate threshold from the raw data. It also performs a spatiotemporal consistency check on the daily precipitation data to identify and remove dead or erroneous values. Additionally, it completes missing precipitation data using interpolation methods. The climate threshold is determined through statistical analysis of historical data from the proposed station and its surrounding meteorological stations and is dynamically adjusted based on geographical and climatic characteristics.

3. The method for assessing rainstorm disaster risk based on objectively identifying rainstorm processes and determining intensity levels according to claim 2, characterized in that, The climate threshold is determined in the following manner: Within a 100km radius of the proposed station, select several long-sequence precipitation stations with similar precipitation probability distributions to the proposed station. The selected stations must have a correlation significance test with the proposed station's precipitation over the past 10 years with a confidence level exceeding 95%, and the altitude difference between the selected stations and the proposed station must not exceed 100m. The variance of daily precipitation at the proposed station and each selected long-sequence precipitation station was calculated separately. The maximum variance of daily precipitation at all stations was taken as three times the climate average value and then added to it as the climate threshold for the proposed station. The raw precipitation data of the proposed station were examined based on the determined climate threshold, and spurious values ​​exceeding the climate threshold were removed.

4. 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 SS2, when determining the rainfall threshold for the proposed station, at least the following sub-steps are included: SS2.1 Based on the long-term precipitation database constructed in step SS1, the maximum daily precipitation of the proposed station over the past 30 years and above is first statistically analyzed and an annual extreme value sequence is formed. SS2.2 sorts the annual extreme value sequence composed of the maximum daily precipitation over the years from smallest to largest, and selects the historical minimum annual maximum daily precipitation as the rainstorm threshold for the proposed station. SS2.3 Determine whether the obtained rainfall threshold for the proposed station is less than 25mm. If it is less than 25mm, then 25mm is taken as the rainfall threshold for the proposed 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, Step SS3, in the process of objectively identifying the rainstorm process and calculating its overall intensity, includes at least the following sub-steps: SS3.1 Based on the long-term precipitation database established in step SS1 and the rainstorm threshold determined in step SS2, the daily precipitation data of each year are traversed sequentially to identify and mark all dates in each year that reach or exceed the rainstorm threshold, and these dates are defined as rainstorm days. SS3.2 Using each marked rainstorm day as the center, the number of precipitation days is increased day by day forward and backward until the consecutive precipitation amounts do not reach the rainstorm threshold, and then the equivalent rainstorm intensity of the rainstorm process after each additional day is calculated: in, Rs ( j , n ) is the first j The number of rainstorm events and their duration were: n The intensity of the rainstorm at that time was quite heavy. n The duration of the rainstorm. a The weighting coefficient is set to 0.

8. The number of days during the rainstorm process is n Average precipitation at that time i =1,2,…, n , R i The first during the rainstorm i Rainfall for the day; SS3.3 For each identified potential rainstorm event, compare the equivalent rainstorm intensity under different combinations of number of days, and select the largest equivalent rainstorm intensity value as the comprehensive intensity of the rainstorm event; SS3.4 The number of days corresponding to the maximum equivalent rainfall intensity is taken as the objectively determined number of days of the rainfall process, and the start and end dates of the corresponding process are taken as the start and end dates of the rainfall process; SS3.5 Repeat steps SS3.2 to SS3.4 to iterate through all marked rainstorm days, identify all rainstorm events throughout the year, and calculate the start and end dates, duration, and overall intensity of each rainstorm event.

6. The method for assessing rainstorm disaster risk based on objective identification of rainstorm processes and determination of intensity levels according to claim 1, characterized in that, In step SS4, fitting the GEV function includes at least the following: SS4.1 Based on the comprehensive intensity of each rainstorm process in each year 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. If there is no rainstorm process that meets the requirements of step SS3 in a certain year, the maximum daily precipitation in that year shall be used as the comprehensive intensity of the rainstorm process in that year. SS4.2 uses the annual extreme value sequence of the comprehensive intensity of rainstorm processes to fit the GEV function, constructs a model of the relationship between the comprehensive intensity of rainstorms and the return period, and uses linear moment estimation or maximum likelihood estimation to statistically obtain the scale parameter, location parameter and shape parameter of the GEV function.

7. 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 SS4, before fitting the GEV function, the stationarity of the annual extreme value sequence is first tested. The Mann-Kendall test or 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 of difference or piecewise fitting to ensure the effectiveness of the GEV function fitting.

8. 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 SS6, when comprehensively assessing the risk of rainstorm disasters by combining the disaster-causing factor information of the area where the proposed station is located, a disaster risk assessment model is further introduced. The specific method is as follows: adopting a disaster risk assessment framework based on three elements of hazard, vulnerability and exposure, the rainstorm intensity level of the proposed station is used as the hazard parameter, the regional exposure is assessed by combining regional population density, economic development level and land use type, and the regional vulnerability is assessed by combining topographic environmental information, so as to comprehensively output the quantitative assessment result of 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 storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements 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.

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