Geological disaster meteorological probability early warning method considering regional rainfall distribution

Abstract

The invention belongs to the technical field of geological disaster prevention and reduction, and relates to a geological disaster meteorological probability early-warning method considering regional rainfall distribution, which comprises the following steps: dividing a research region into a plurality of early-warning subareas, and calculating the early-warning subareas according to the actual rainfall values corresponding to each early-warning subarea at different forecast rainfall levels; establishing a daily scale forecast-live rainfall probability distribution model; acquiring an early-warning index sample set corresponding to the occurrence of historical geological disasters in each early-warning subarea, determining a minimum value Tmin in the early-warning index sample set, and taking T = Tmin as an early-warning limit state of the current early-warning subarea in the statistical sense; and calculating failure probabilities of all the early warning units in each early warning partition, determining early warning levels of all the early warning units in each early warning partition according to current early warning specifications, and forming an early warning result of the research area. According to the invention, the failure probability is introduced to represent the possibility of regional scale geological disaster occurrence, and the accuracy of geological disaster meteorological risk early warning can be effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of geological disaster prevention and mitigation, and relates to a geological disaster meteorological probability early warning method taking regional rainfall distribution into consideration. Background Art

[0002] Among the many prevention measures, meteorological risk warning of geological disasters is one of the effective ways to prevent geological disasters induced by rainfall. Past practice has shown that timely organization of people's evacuation based on accurate regional geological disaster warning information can often effectively avoid tragedies of casualties and have significant disaster prevention and mitigation effects.

[0003] However, due to the complexity of the interaction of weather systems at various scales, rainfall forecasting is a very complex problem. It is difficult to avoid that the actual rainfall is inconsistent with the forecast rainfall, or even that there is a difference in magnitude, which directly affects the accuracy of geological disaster meteorological warning forecasts based on forecast rainfall as the main trigger factor. Therefore, how to reasonably consider the uncertainty of rainfall distribution has become the key to improving the accuracy of geological disaster meteorological warning forecasts.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a geological disaster meteorological probability early warning method taking into account regional rainfall distribution.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] This geological disaster meteorological probabilistic warning method, which takes into account regional rainfall distribution, introduces failure probability to characterize the possibility of regional-scale geological disasters in the early warning of rainfall-induced geological disaster risks, thereby improving the accuracy of geological disaster meteorological risk warnings.

[0008] Specifically, the geological disaster meteorological risk early warning method comprises the following steps:

[0009] Step 1: Divide the study area into multiple warning zones, and establish a daily-scale forecast-actual rainfall probability distribution model based on the actual rainfall values ​​corresponding to each warning zone at different forecast rainfall levels;

[0010] Step 2: Obtain the warning index sample set corresponding to the historical geological disasters in each warning zone, and determine the minimum value T in the warning index sample set. min , with T = T min As the warning limit state in the statistical sense of the current warning zone;

[0011] Step 3: Calculate the failure probability of all warning units in each warning zone, determine the warning level of all warning units in each warning zone according to the current warning specifications, and form the warning results of the study area.

[0012] Step 1 is as follows: using the daily historical forecast rainfall contour lines and the quantitative precipitation estimation data of the corresponding time as source data, the study area is refined into multiple warning zones; the daily rainfall R d It is regarded as a random variable, and the actual rainfall values ​​of each warning zone are classified and counted according to the current forecast rainfall level standards of the meteorological department, and a forecast-actual rainfall probability distribution model on a daily scale is established.

[0013] Specifically, the process of obtaining the early warning index sample set in step 2 is as follows:

[0014] Different types of geological disasters in the study area were selected as statistical samples, the potential degree of each geological disaster and the actual rainfall at the time of the disaster were input, and the warning index of each geological disaster was calculated according to the current warning model. The warning index sample set {T1, T2, T3…T m It should be noted that in the process of obtaining the early warning index sample set, the early warning index sample should be supplemented according to the changes in the geological environment conditions of the early warning zone after the end of the flood season each year, combined with the latest minimum value T in the early warning index sample set. min Update the warning limit state in the statistical sense of the warning zone.

[0015] Furthermore, the different types of geological disasters include landslides, collapses or mud-rock flows directly triggered by heavy rainfall.

[0016] Specifically, in the current warning specification "Geological Hazard Meteorological Risk Warning Specification" (DZ / T0449-2023), the mathematical expression of the warning model is as follows:

[0017] T=f(G,R d ,R p ) Formula (1)

[0018] In formula (1), T is the early warning index; G is the potential degree of geological disasters; when conducting early warning analysis, R d is the daily rainfall (forecast value); R P It is the precipitation process before the occurrence of geological disasters, and the effective rainfall that has an impact on geological disasters.

[0019] Specifically, step 3 includes:

[0020] Step 3.1, sampling to determine the rainfall distribution of each warning unit in the warning area A: For each warning unit, when carrying out the geological disaster meteorological risk warning calculation, according to the level of forecast rainfall, sampling is performed according to the forecast-actual rainfall probability distribution model to form the possible rainfall value distribution R of the corresponding warning unit. d1 ,R d2 ,R d3 ,…,R dN , N is the total number of samples;

[0021] Step 3.2: Calculate the failure probability of each warning unit in the warning zone A: Take the minimum value T in the warning index sample set in the warning zone A as min As the critical disaster threshold of warning zone A, the possible rainfall value distribution R of each warning unit in warning zone A is d1 ,R d2 ,R d3 ,…,R dN Input the current warning model, calculate the warning index distribution of each warning unit in the warning area A, and then calculate the failure probability P of each warning unit. f ;

[0022] Step 3.3: Determine the warning level: According to the failure probability P of each warning unit f , determine the warning level of each warning unit in combination with the current warning standards, and determine the warning level of all warning units in warning zone A;

[0023] Step 3.4: Repeat steps 3.1 to 3.3 to determine the warning levels of all warning units in all warning zones one by one, and finally form the warning results of the study area.

[0024] Furthermore, the failure probability P in step 3.3 f The calculation formula is as follows:

[0025]

[0026] In formula (2), n is the warning index T≤T in the warning index distribution of the same warning unit min is the number of samples, and N is the total number of samples.

[0027] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0028] The present invention is based on the regional geological disaster meteorological early warning statistical model method widely used in my country, fully considers the uncertainty of forecast rainfall, a key interference factor, and establishes a forecast-actual rainfall distribution probability model through statistical analysis of historical data (actual rainfall); adopts early warning refinement zoning combined with engineering analogy method to determine the early warning limit state under ideal homogenization conditions, and then introduces reliability theory to sample and calculate the possible rainfall distribution and failure probability of each early warning unit, so as to realize the probability early warning of the risk of geological disasters triggered by rainfall. The present invention can promote the implementation of the probability of geological disasters corresponding to each warning level in the current geological disaster meteorological risk early warning standard specification, significantly improve the accuracy of geological disaster meteorological early warning, and better serve geological disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are incorporated in and constitute a part of this specification and, together with the description, serve to explain the principles of the present invention.

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0031] Figure 1 A flow chart of a meteorological probability early warning method for geological disasters taking into account regional rainfall distribution provided by the present invention;

[0032] Figure 2 for Figure 1 Flow chart of the specific implementation of step 3;

[0033] Figure 3 A schematic diagram of dividing the study area provided in Example 1 of the present invention into four early warning zones;

[0034] Figure 4 A schematic diagram showing the division of the warning zone A provided in Example 1 of the present invention into 14 warning units;

[0035] Figure 5 A failure probability distribution diagram of all early warning units in the study area provided in Example 1 of the present invention;

[0036] Figure 6 This is a distribution map of early warning results for the research area provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0037] Here, exemplary embodiments will be described in detail, and the embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are only examples consistent with some aspects of the present invention described in detail in the appended claims.

[0038] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0039] Example

[0040] See also Figure 1 This embodiment provides a geological disaster meteorological probability warning method that takes into account regional rainfall distribution. In the early warning of the risk of geological disasters induced by rainfall, the failure probability is introduced to characterize the possibility of regional-scale geological disasters, thereby improving the accuracy of geological disaster meteorological risk warning. Specifically, the method includes the following steps:

[0041] Step 1: Divide the study area into multiple warning zones, and establish a daily-scale forecast-actual rainfall probability distribution model based on the actual rainfall values ​​corresponding to each warning zone at different forecast rainfall levels;

[0042] Specifically, the step 1 is as follows: using the daily historical forecast rainfall contour lines and the quantitative precipitation estimation data of the corresponding time as source data, the study area is refined into multiple warning zones; the daily rainfall R d It is regarded as a random variable, and the actual rainfall values ​​of each warning zone are classified and counted according to the current forecast rainfall level standard of the meteorological department, and a daily-scale forecast-actual rainfall probability distribution model is established. It should be noted that the actual rainfall value here means the actual rainfall. There are many kinds of measured values ​​of actual rainfall, such as quantitative precipitation estimation data and precipitation observation data of rain gauge stations. In this embodiment, quantitative precipitation estimation data is preferably used as source data, which can supplement the rainfall distribution in the area without rain gauge observation. Its real-time nature not only helps users to grasp the rainfall situation in time, but also provides strong support for regional-scale warning and forecast.

[0043] In this embodiment, the study area is divided into four early warning zones: A, B, C, and D according to watershed, water system, regional climate, average annual rainfall, geological environmental conditions, and historical geological disaster density. Figure 3). Taking the daily historical forecast rainfall contour lines and the quantitative precipitation estimation data of the corresponding time in the past five years as the source data, the actual rainfall values ​​corresponding to the four warning zones when the forecast rainfall is light rain (<10mm), moderate rain (10-25mm), heavy rain (25-50mm), rainstorm (50-100mm), heavy rainstorm (100-250mm) and extremely heavy rainstorm (>250mm) are counted respectively, and the actual rainfall probability distribution model is established when the daily forecast rainfall is light rain, moderate rain, heavy rain, rainstorm, heavy rainstorm and extremely heavy rainstorm, such as R d ~N(μ,σ 2 ).

[0044] Furthermore, taking the warning zone A as an example, after fitting test, the forecast-actual rainfall of the warning zone A conforms to the gamma distribution; among them, the actual rainfall probability distribution model when the forecast rainfall is light rain level is R1~Γ(1.3,10.5), the actual rainfall probability distribution model when the forecast rainfall is moderate rain level is R2~Γ(1.9,16.2), the actual rainfall probability distribution model when the forecast rainfall is heavy rain level is R3~Γ(3.5,12.3), the actual rainfall probability distribution model when the forecast rainfall is torrential rain level is R4~Γ(4.8,13.5), the actual rainfall probability distribution model when the forecast rainfall is heavy rain level is R5~Γ(5.6,7.6), and the actual rainfall probability distribution model when the forecast rainfall is extremely heavy rain level is R6~Γ(7.1,9.4).

[0045] Step 2: Obtain the warning index sample set corresponding to the historical geological disasters in each warning zone, and determine the minimum value T in the warning index sample set. min , with T = T min As the warning limit state in the statistical sense of the current warning zone;

[0046] Specifically, in step 2, the process of obtaining the early warning index sample set is as follows:

[0047] The geological disaster cases in the study area were screened, and the landslides, collapses and debris flows directly triggered by heavy rainfall were taken as statistical samples (a total of 6765 cases). The potential degree of each geological disaster and the actual rainfall at the time of the disaster were input, and the warning index of each geological disaster was calculated according to the current statistical early warning model (Formula 3). The early warning index sample set {T1, T2, T3…T 6765}, where the minimum value T of the sample set min =0.35, which represents the warning limit state in the statistical sense of the warning zone.

[0048] It should be noted that in the process of obtaining the early warning index sample set, the early warning index sample should be supplemented according to the changes in the geological environmental conditions of the early warning zone after the end of the flood season each year, combined with the latest minimum value T in the early warning index sample set. min Update the warning limit state in the statistical sense of the warning zone. Here, the flood season refers to the period of regular and significant rise in river water levels caused by seasonal rainfall, ice melting, and snow melting. Due to differences in geographical location, weather system, etc., the flood seasons of my country's seven major rivers vary; according to the rainfall, flood occurrence patterns and meteorological causes, the flood seasons are roughly divided as follows: Pearl River: April to September, Yangtze River: May to October, Huaihe River: June to September, Yellow River: June to October, Haihe River: June to September, Liaohe River: June to September, Songhua River: June to September.

[0049] Furthermore, according to the "Geological Hazard Meteorological Risk Warning Specification" (DZ / T 0449-2023), the general formula of the current warning model is as follows:

[0050] T=G×(R d +R p ) Formula (3)

[0051] In formula (3), the meanings of the parameters are as follows:

[0052] T is the warning index, according to which the geological disaster meteorological risk warning level is determined;

[0053] G is the potential degree of geological disasters, which is used as a quantitative indicator of geological environmental conditions;

[0054] R d is the rainfall on that day: When a geological disaster actually occurs, R d is the rainfall on that day (actual value); when conducting early warning analysis before a disaster occurs, R d is the rainfall for the day (forecast value);

[0055] R p It is the previous effective rainfall, that is, the precipitation process before the geological disaster occurs, and the effective rainfall that has an impact on the disaster.

[0056] Step 3: Calculate the failure probability of all warning units in each warning zone, determine the warning level of all warning units in each warning zone according to the current warning model, and form the warning results of the study area.

[0057] Specifically, see Figure 2 , step 3 specifically includes:

[0058] Step 3.1, sampling to determine the rainfall distribution of each warning unit in the warning area A: For each warning unit, when carrying out the geological disaster meteorological risk warning calculation, according to the level of forecast rainfall, sampling is performed according to the forecast-actual rainfall probability distribution model to form the possible rainfall value distribution R of the corresponding warning unit. d1 ,R d2 ,R d3 ,…,R dN , N is the total number of samples;

[0059] In this embodiment, taking the warning units located at multiple forecast rainfall levels as an example, the rainfall level with the largest area share is selected as the forecast rainfall level of the warning unit. Then, in the warning area A, 3 warning units forecast the rainfall as torrential rain (100-250 mm), 7 warning units forecast the rainfall as torrential rain (50-100 mm), and 4 warning units forecast the rainfall as heavy rain (10-25 mm) (see 4).

[0060] Sampling 1000 times according to the rainfall probability distribution of torrential rain results in the distribution of possible rainfall values ​​{151, 74, 96, 134, ..., 126}, sampling 1000 times according to the rainfall probability distribution of torrential rain results in the distribution of possible rainfall values ​​{68, 44, 112, 79, ..., 85}, sampling 1000 times according to the rainfall probability distribution of heavy rain results in the distribution of possible rainfall values ​​{51, 23, 36, 43, ..., 77}.

[0061] Step 3.2: Calculate the failure probability of each warning unit in the warning zone A: Take the minimum value T in the warning index sample set in the warning zone A as min =0.35 as the critical disaster threshold of warning zone A; according to the current warning model, i.e., formula (3), the possible rainfall value distribution R corresponding to each warning unit in warning zone A is respectively d1 ,R d2 ,R d3 ,…,R dN Input the current warning model, calculate the warning index distribution of each warning unit in the warning area A, and then calculate the failure probability P of each warning unit. f .

[0062] For example, combined with Figure 3 , the rainfall forecast for warning unit 1 in warning zone A is heavy rain. Substituting the possible rainfall distribution {151, 74, 96, 134, ..., 126} into the warning model, that is, formula (3), the warning index distribution of warning unit 1 is calculated to be {0.23, 0.88, 0.76, 0.34, ..., 0.42}. According to statistics, the number of samples n with warning index T ≤ 0.35 is 417, so the warning failure probability of warning unit 1 is 41.7%. The calculation process is:

[0063]

[0064] Step 3.3: Determine the warning level: According to the failure probability P of each warning unit f , determine the warning level of each warning unit in combination with the current warning standards, and determine the warning level of all warning units in warning area A.

[0065] For example, according to the current warning standards, the warning level of warning unit 1 is determined to be orange warning (40% <P f ≤60%); Similarly, according to the failure probability P of all warning units in warning zone A f , determine the warning level of each warning unit in combination with the current warning standards, until the warning level of all warning units in warning area A is determined.

[0066] Step 3.4: Determine the warning results of all warning units in each warning zone: Repeat steps 3.1 to 3.2 to determine the failure probability P of all warning units in each warning zone (B, C, D) one by one. f Repeat step 3.3 to determine the warning level of all warning units in each warning zone, see Figure 5 , and finally form the early warning results of the study area, see Figure 6 .

[0067] The geological disaster meteorological risk early warning method provided by the present invention adopts reliability theory to analyze the uncertainty of rainfall distribution (in terms of failure probability P f Characterization), and then carry out probabilistic early warning of the risk of geological disasters triggered by rainfall, which can effectively improve the accuracy of meteorological early warning of geological disasters and better serve the field of geological disaster prevention and mitigation.

[0068] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention.

[0069] It should be understood that the present invention is not limited to what has been described above and that various modifications and changes may be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A geological disaster meteorological probability early warning method considering regional rainfall distribution, characterized in that: In the early warning of the risk of rainfall-induced geological disasters, the failure probability is introduced to characterize the possibility of regional-scale geological disasters, thereby improving the accuracy of meteorological risk warnings for geological disasters.

2. The method for meteorological probability early warning of geological disasters considering regional rainfall distribution according to claim 1 is characterized in that: The steps include: Step 1: Divide the study area into multiple warning zones, and establish a daily-scale forecast-actual rainfall probability distribution model based on the actual rainfall values ​​corresponding to each warning zone at different forecast rainfall levels; Step 2: Obtain the warning index sample set corresponding to the historical geological disasters in each warning zone, and determine the minimum value T in the warning index sample set. min , with T = T min As the warning limit state in the statistical sense of the current warning zone; Step 3: Calculate the failure probability of all warning units in each warning zone, determine the warning level of all warning units in each warning zone according to the current warning specifications, and form the warning results of the study area.

3. The meteorological probability early warning method for geological disasters considering regional rainfall distribution according to claim 2 is characterized in that: The step 1 is specifically as follows: The daily historical rainfall forecast contours and the quantitative precipitation estimation data of the corresponding time are used as source data to refine the study area into multiple warning zones. d It is regarded as a random variable, and the actual rainfall values ​​of each warning zone are classified and counted according to the current forecast rainfall level standards of the meteorological department, and a forecast-actual rainfall probability distribution model on a daily scale is established.

4. The method for meteorological probability early warning of geological disasters considering regional rainfall distribution according to claim 2 is characterized in that: The process of obtaining the early warning index sample set in step 2 is as follows: Different types of geological disasters in the study area were selected as statistical samples, the potential degree of each geological disaster and the actual rainfall at the time of the disaster were input, and the warning index of each geological disaster was calculated according to the current warning model. The warning index sample set {T1, T2, T3…T m }.

5. The method for meteorological probability early warning of geological disasters considering regional rainfall distribution according to claim 4 is characterized in that: In the process of obtaining the early warning index sample set, the early warning index samples should be supplemented according to the changes in the geological environmental conditions of the early warning zone after the end of the flood season each year, and the latest minimum value T in the early warning index sample set should be combined. min Update the warning limit state in the statistical sense of the warning zone.

6. The method for meteorological probability early warning of geological disasters considering regional rainfall distribution according to claim 4 is characterized in that: The mathematical expression of the current early warning model is as follows: T=f(G,R d ,R P ) Formula (1) In formula (1), T is the early warning index; G is the potential degree of geological disasters; when conducting early warning analysis, R d is the rainfall on that day; R P It is the precipitation process before the occurrence of geological disasters, and the effective rainfall that has an impact on geological disasters.

7. The geological disaster meteorological probability early warning method considering regional rainfall distribution according to claim 4 is characterized in that: The different types of geological disasters include landslides, collapses or mud-rock flows directly triggered by heavy rainfall.

8. The method for meteorological probability early warning of geological disasters considering regional rainfall distribution according to claim 2 is characterized in that: The step 3 specifically includes: Step 3.1, sampling to determine the rainfall distribution of each warning unit in the warning area A: For each warning unit, when carrying out the geological disaster meteorological risk warning calculation, according to the level of forecast rainfall, sampling is performed according to the forecast-actual rainfall probability distribution model to form the possible rainfall value distribution R of the corresponding warning unit. d1 ,R d2 ,R d3 ,…,R dN , N is the total number of samples; Step 3.2: Calculate the failure probability of each warning unit in the warning zone A: Take the minimum value T in the warning index sample set in the warning zone A as min As the critical disaster threshold of warning zone A, the possible rainfall value distribution R of each warning unit in warning zone A is d1 ,R d2 ,R d3 ,…,R dN Input the current warning model, calculate the warning index distribution of each warning unit in the warning area A, and then calculate the failure probability P of each warning unit. f ; Step 3.3: Determine the warning level: According to the failure probability P of each warning unit f , determine the warning level of each warning unit in combination with the current warning standards, and determine the warning level of all warning units in warning zone A; Step 3.4: Repeat steps 3.1 to 3.3 to determine the warning levels of all warning units in all warning zones one by one, and finally form the warning results of the study area.

9. The method for meteorological probability early warning of geological disasters considering regional rainfall distribution according to claim 8 is characterized in that: The failure probability P in step 3.3 f The calculation formula is as follows: In formula (2), n is the warning index T≤T in the warning index distribution of the same warning unit min is the number of samples, and N is the total number of samples.

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