Geological disaster meteorological probability early warning method considering regional rainfall distribution

Abstract

This invention belongs to the field of geological disaster prevention and mitigation technology, and relates to a geological disaster meteorological probability early warning method considering regional rainfall distribution. The method includes the following steps: dividing the study area into multiple early warning zones; establishing a daily-scale forecast-actual rainfall probability distribution model based on the actual rainfall values ​​corresponding to different forecast rainfall levels in each early warning zone; obtaining a sample set of early warning indices corresponding to historical geological disaster occurrences within each early warning zone; and determining the minimum value T in the early warning index sample set. min , with T=T min This invention uses failure probability to characterize the likelihood of geological disasters occurring at the regional scale, as the current statistical definition of the warning zone's extreme state. It calculates the failure probability of all warning units within each warning zone, determines the warning level of all warning units within each zone according to current warning standards, and forms the warning results for the study area.

Description

Technical Field

[0001] This invention belongs to the field of geological disaster prevention and mitigation technology, and relates to a geological disaster meteorological probability early warning method that takes into account regional rainfall distribution. Background Technology

[0002] Among numerous preventative measures, geological disaster meteorological risk early warning is one of the effective ways to prevent rainfall-induced geological disasters. Past practices have shown that, based on accurate regional geological disaster early warning information, timely organization of the evacuation of the masses can often effectively avoid tragedies of casualties, and the disaster prevention and mitigation effects are significant.

[0003] However, due to the complexity of interactions between weather systems at various scales, rainfall forecasting is a very complex problem. Discrepancies between actual and forecasted rainfall, and even differences in magnitude, are unavoidable, directly impacting the accuracy of meteorological early warnings and forecasts for geological disasters that rely primarily on forecasted rainfall. Therefore, rationally considering the uncertainties in rainfall distribution is crucial to improving the accuracy of meteorological early warnings and forecasts for geological disasters.

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

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a geological disaster meteorological probability early warning method that takes into account regional rainfall distribution.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

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

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

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

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

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

[0012] Specifically, step 1 involves using historical daily forecast rainfall isohyets and corresponding quantitative precipitation estimation data as source data to refine the study area into multiple early warning zones; and then calculating the daily rainfall R... d Treating them as random variables, the actual rainfall values ​​of each warning zone are statistically analyzed according to the current forecast rainfall level standards of the meteorological department, and a daily-scale forecast-actual rainfall probability distribution model 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 hazards within the study area are selected as statistical samples. The potential degree of each geological hazard and the actual rainfall at the time of occurrence are input. The early warning index of each geological hazard is calculated according to the current early warning model, resulting in a sample set of early warning indices {T1, T2, T3…T} corresponding to the occurrence of historical geological hazards in each early warning zone. 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 each flood season, and 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 hazards include landslides, collapses, or debris flows directly triggered by heavy rainfall.

[0016] Specifically, in the current early warning standard "Geological Disaster Meteorological Risk Early Warning Standard" (DZ / T0449-2023), the mathematical expression of the early warning model is as follows:

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

[0018] In equation (1), T is the early warning index; G is the potential degree of geological disaster; when conducting early warning analysis, R... d This represents the predicted rainfall for the day; R P This refers to the effective rainfall amount that has an impact on geological disasters during the precipitation process preceding their occurrence.

[0019] Specifically, step 3 includes:

[0020] Step 3.1: Sampling to determine the rainfall distribution of each early warning unit within early warning zone A: For each early warning unit, when conducting geological disaster meteorological risk early warning calculations, sampling is performed according to the forecast rainfall level and the forecast-actual rainfall probability distribution model to form the possible rainfall value distribution R for the corresponding early 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 early warning unit within early warning zone A: using the minimum value T from the sample set of early warning indices within early warning zone A. min As the critical disaster threshold for warning zone A, the possible rainfall values ​​R of each warning unit within warning zone A are distributed. d1 ,R d2 ,R d3 ,…,R dN Input the existing early warning model, calculate the early warning index distribution of each early warning unit within early warning zone A, and then calculate the failure probability P of each early warning unit. f ;

[0022] Step 3.3: Determine the warning level: Based on the failure probability P of each warning unit. f Based on the existing early warning standards, the early warning level of each early warning unit is determined, and the early warning level of all early warning units within early warning zone A is determined.

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

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

[0025]

[0026] In equation (2), n represents the early warning index distribution of the same early warning unit, where T≤T min The number of samples, 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] This invention is based on the statistical model method for regional geological disaster meteorological early warning widely used in my country. It fully considers the key interference factor of forecasted rainfall uncertainty. Through statistical analysis of historical data (actual rainfall), it establishes a probability model for the distribution of forecasted-actual rainfall. It employs a refined zoning approach combined with engineering analogy to determine the warning limit state under ideal homogenization conditions. Furthermore, it introduces reliability theory to sample and calculate the possible rainfall distribution and failure probability of each warning unit, achieving probabilistic early warning of the risk of geological disasters triggered by rainfall. This invention can promote the implementation of the geological disaster occurrence probabilities corresponding to each warning level in the current geological disaster meteorological risk early warning standards and specifications, significantly improve the accuracy of geological disaster meteorological early warnings, and better serve geological disaster prevention and mitigation. Attached Figure Description

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

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart of a geological disaster meteorological probability early warning method considering regional rainfall distribution provided by the present invention;

[0032] Figure 2 for Figure 1 The flowchart for the specific implementation of step 3;

[0033] Figure 3 This is a schematic diagram showing the division of the research area into four early warning zones, as provided in Embodiment 1 of the present invention.

[0034] Figure 4 This is a schematic diagram showing the division of early warning zone A into 14 early warning units according to Embodiment 1 of the present invention;

[0035] Figure 5 This is a failure probability distribution diagram of all early warning units within the study area provided in Embodiment 1 of the present invention;

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

[0037] Exemplary embodiments will now be described in detail. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples consistent with some aspects of the invention as detailed in the appended claims.

[0038] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0039] Example

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

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

[0042] Specifically, step 1 involves: using daily-scale historical forecast rainfall isohyets and corresponding quantitative precipitation estimation data as source data, refining the study area into multiple early warning zones; and calculating the daily rainfall R... d Treating the rainfall as a random variable, the actual rainfall values ​​for each warning zone are statistically analyzed according to the current forecast rainfall level standards 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 refers to the actual rainfall amount. Actual rainfall can be measured in many ways, such as quantitative precipitation estimation data and rain gauge observation data. In this embodiment, quantitative precipitation estimation data is preferred as the source data. This data can supplement the rainfall distribution in areas without rain gauge observations. Its real-time nature not only helps users to grasp the rainfall situation in a timely manner but also provides strong support for regional-scale early warning and forecasting.

[0043] In this embodiment, the study area is divided into four early warning zones: A, B, C, and D, based on watersheds, river systems, regional climate, average annual rainfall, geological environmental conditions, and historical geological disaster density. Figure 3Using historical daily-scale forecast rainfall isohyets and corresponding quantitative precipitation estimation data from the past five years as source data, the actual rainfall values ​​corresponding to the forecast rainfall of light rain (<10mm), moderate rain (10-25mm), heavy rain (25-50mm), torrential rain (50-100mm), heavy torrential rain (100-250mm), and extremely heavy torrential rain (>250mm) were statistically analyzed for four warning zones. A probability distribution model of actual rainfall for daily-scale forecast rainfall of light rain, moderate rain, heavy rain, torrential rain, heavy torrential rain, and extremely heavy torrential rain was established, such as R... d ~N(μ,σ 2 ).

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

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

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

[0047] Geological disaster cases within the study area were selected, with landslides, collapses, and debris flows directly triggered by heavy rainfall as the statistical sample (a total of 6765 cases). The potential degree of each geological disaster and the actual rainfall at the time of occurrence were input. The early warning index of each geological disaster was calculated according to the current statistical early warning model (Equation 3), and a sample set of early warning indices {T1, T2, T3…T...} for historical disasters within each early warning zone was statistically formed. 6765}, where the minimum value T of the sample set is... min =0.35, and this value represents the statistically significant warning limit state of this warning zone.

[0048] It should be noted that during 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 zones after the end of each flood season, and combined with the latest minimum value T in the early warning index sample set. min Update the warning zone statistical definition of the warning limit state. Here, the flood season refers to the period of year in which river water levels rise significantly and regularly due to seasonal rainfall, ice melting, and snowmelt. Due to differences in geographical location, weather systems, and other factors, the flood seasons of my country's seven major rivers vary in timing. Based on the analysis of 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, Huai River: June to September, Yellow River: June to October, Hai River: June to September, Liao River: June to September, Songhua River: June to September.

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

[0050] T = G × (R) d +R p Equation (3)

[0051] In equation (3), the meanings of each parameter are as follows:

[0052] T is the early warning index, and the meteorological risk warning level for geological disasters is determined based on this early warning index.

[0053] G represents the potential for geological hazards, used as a quantitative indicator of geological environmental conditions.

[0054] R d The rainfall amount for that day: R is the actual rainfall at the time the geological disaster occurred. d This represents the actual rainfall for the day; R is used when conducting early warning analysis before a disaster occurs. d This represents the predicted rainfall for the day.

[0055] R p Effective rainfall in the preceding period refers to the precipitation process before a geological disaster occurs, and the effective rainfall that has an impact on the disaster.

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

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

[0058] Step 3.1: Sampling to determine the rainfall distribution of each early warning unit within early warning zone A: For each early warning unit, when conducting geological disaster meteorological risk early warning calculations, sampling is performed according to the forecast rainfall level and the forecast-actual rainfall probability distribution model to form the possible rainfall value distribution R for the corresponding early warning unit. d1 ,R d2 ,R d3 ,…,R dN N is the total number of samples;

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

[0060] Sampling 1000 times based on the probability distribution of heavy rainstorms yields a possible rainfall value distribution {151,74,96,134,……,126}; sampling 1000 times based on the probability distribution of rainstorms yields a possible rainfall value distribution {68,44,112,79,……,85}; and sampling 1000 times based on the probability distribution of heavy rain yields a possible rainfall value distribution {51,23,36,43,……,77}.

[0061] Step 3.2: Calculate the failure probability of each early warning unit within early warning zone A: using the minimum value T from the sample set of early warning indices within early warning zone A. min =0.35 is used as the critical disaster threshold for warning zone A; according to the current warning model, i.e., equation (3), the possible rainfall values ​​R corresponding to each warning unit in warning zone A are distributed. d1 ,R d2 ,R d3 ,…,R dN Input the existing early warning model, calculate the early warning index distribution of each early warning unit within early warning zone A, and then calculate the failure probability P of each early warning unit. f .

[0062] For example, combining Figure 3 The forecast rainfall for early warning unit 1 in early warning zone A is a heavy rainstorm. Substituting the possible rainfall distribution {151,74,96,134,……,126} into the early warning model, i.e., equation (3), we calculate the early warning index distribution {0.23,0.88,0.76,0.34,……,0.42} for early warning unit 1. According to statistics, the number of samples n with early warning index T≤0.35 is 417. Therefore, the probability of early warning failure for early warning unit 1 is 41.7%. The calculation process is as follows:

[0063]

[0064] Step 3.3: Determine the warning level: Based on the failure probability P of each warning unit. f Based on the existing early warning standards, the early warning level of each early warning unit is determined, and the early warning level of all early warning units within early warning zone A is determined.

[0065] For example, according to the current early warning standards, the early warning level of early warning unit 1 is determined to be orange (40%). <P f ≤60%); Similarly, based on the failure probability P of all early warning units within early warning zone A. f The warning level of each warning unit is determined by combining the existing warning standards, until the warning level of all warning units in warning zone A is determined.

[0066] Step 3.4: Determine the warning results of all warning units within each warning zone: Repeat steps 3.1 to 3.2 above to determine the failure probability P of all warning units within each warning zone (B, C, D). f Repeat step 3.3 to determine the warning level of all warning units within each warning zone, see... Figure 5 The final early warning results for the study area are shown below. Figure 6 .

[0067] The geological disaster meteorological risk early warning method provided by this invention uses reliability theory to analyze the uncertainty of rainfall distribution (with failure probability P). f (Characteristics), and then carry out probability 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 merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention.

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

Claims

1. A method for geological disaster meteorological probability early warning considering regional rainfall distribution, characterized in that, In the early warning of the risk of rainfall-induced geological disasters, failure probability is introduced to characterize the possibility of geological disasters occurring at the regional scale, thereby improving the accuracy of meteorological risk warnings for geological disasters. The geological disaster meteorological probability early warning method includes the following steps: Step 1: Divide the study area into multiple warning zones. Based on the actual rainfall values ​​corresponding to different forecast rainfall levels in each warning zone, establish a daily-scale forecast-actual rainfall probability distribution model. Step 2: Obtain the early warning index sample set corresponding to the historical geological disaster occurrences within each early warning zone, and determine the minimum value in the early warning index sample set. ,by As the current early warning zone statistical sense of the extreme state of early warning; Step 3: Calculate the failure probability of all early warning units in each early warning zone, determine the early warning level of all early warning units in each early warning zone according to the current early warning specifications, and form the early warning results for the study area; The process of obtaining the early warning index sample set in step 2 is as follows: Different types of geological hazards within the study area were selected as statistical samples. The potential degree of each geological hazard and the actual rainfall at the time of occurrence were input. The early warning index of each geological hazard was calculated according to the current early warning model, resulting in a sample set of early warning indices corresponding to historical geological hazards in each early warning zone. ; During 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 zones after the end of each flood season, and combined with the latest minimum value in the early warning index sample set. Update the warning limit state in the statistical sense of the warning zone.

2. The geological disaster meteorological probability early warning method considering regional rainfall distribution according to claim 1, characterized in that, Step 1 specifically involves: Using daily-scale historical forecast rainfall isohyets and corresponding quantitative precipitation estimation data as source data, the study area was refined into multiple early warning zones; the daily rainfall R... d Treating them as random variables, the actual rainfall values ​​of each warning zone are statistically analyzed according to the current forecast rainfall level standards of the meteorological department, and a daily-scale forecast-actual rainfall probability distribution model is established.

3. The geological disaster meteorological probability early warning method considering regional rainfall distribution according to claim 1, characterized in that, The mathematical expression of the current early warning model is as follows: Equation (1) In equation (1), This is an early warning index; This represents the potential for geological disasters; when conducting early warning analysis, This represents the rainfall for that day. This refers to the effective rainfall amount that has an impact on geological disasters during the precipitation process preceding their occurrence.

4. The geological disaster meteorological probability early warning method considering regional rainfall distribution according to claim 1, characterized in that, The different types of geological hazards include landslides, collapses, or debris flows directly triggered by heavy rainfall.

5. The geological disaster meteorological probability early warning method considering regional rainfall distribution according to claim 1, characterized in that, Step 3 specifically includes: Step 3.1: Sampling to determine the rainfall distribution of each early warning unit within early warning zone A: For each early warning unit, when conducting geological disaster meteorological risk early warning calculations, sampling is performed according to the forecast rainfall level and the forecast-actual rainfall probability distribution model to form the possible rainfall value distribution for the corresponding early warning unit. , The total number of samples; Step 3.2: Calculate the failure probability of each early warning unit within early warning zone A: using the minimum value from the sample set of early warning indices within early warning zone A. As the critical disaster threshold for warning zone A, the possible rainfall values ​​for each warning unit within warning zone A are distributed. Input the existing early warning model, calculate the early warning index distribution of each early warning unit within early warning zone A, and then calculate the failure probability of each early warning unit. ; Step 3.3: Determine the warning level: based on the failure probability of each warning unit. Based on the existing early warning standards, the early warning level of each early warning unit is determined, and the early warning level of all early warning units within early warning zone A is determined. Step 3.4: Repeat steps 3.1 to 3.3 to determine the warning level of all warning units in all warning zones one by one, and finally form the warning results for the study area.

6. The geological disaster meteorological probability early warning method considering regional rainfall distribution according to claim 5, characterized in that, Failure probability in step 3.3 The calculation formula is as follows: Equation (2) In equation (2), n The distribution of warning indices for the same warning unit The number of samples, N This represents the total number of samples.

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