High-risk rainstorm zoning method and system based on hydrometeorological space linear moment frequency analysis

Abstract

The invention provides a rainstorm high-risk zoning method and system based on hydrometeorological space linear moment frequency analysis, and relates to the technical field of flood control and disaster mitigation. A rainfall simulation model can fully reflect hydrological characteristics of a to-be-monitored region by performing detailed slope division and representative acquisition point setting on the to-be-monitored region; therefore, the accuracy of surface runoff prediction is improved; secondly, in combination with historical hydrometeorological data and real-time meteorological conditions, risk assessment can be dynamically adjusted to adapt to constantly changing climatic environments, and more accurate flood risk early warning is provided; besides, comprehensive analysis of root growth characteristic data in the method enhances understanding of soil moisture change, so that in practical application, making of a more effective disaster prevention and reduction strategy is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of hydrometeorological technologies for flood control and disaster reduction, and specifically to a method and system for heavy rain high-risk zoning based on hydrometeorological spatial linear moment frequency analysis. Background Art

[0002] Traditional rainfall risk assessments usually rely on the combination of historical rainfall data and hydrological models. By establishing statistical models based on terrain, soil, and meteorological conditions, the rainfall and flood risk within a specific area are predicted. However, existing technologies mainly focus on static analysis and fail to fully consider the important roles of spatial factors and temporal dynamics in heavy rain risks. Therefore, their application effects in high-slope areas are particularly insufficient. In addition, when dealing with complex terrains and their diversion characteristics, existing methods often struggle to achieve accurate risk assessments, resulting in a serious lack of accuracy in risk judgment and disaster prevention decision-making, and failing to effectively meet the actual application requirements. In the prior art, the publication number is CN112347652B, and the name is a method for heavy rain high-risk zoning based on hydrometeorological regional linear moment frequency analysis, which relates to the field of hydrometeorological technologies for flood control and disaster reduction. The specific solution is as follows: It includes the following steps: S1: Data collection, screening, and quality control; S2: Analysis of the applicability and superiority of the "regional linear moment method"; S3: Division of hydrometeorological consistent areas; S4: Optimal distribution linear selection for consistent areas; S5: Calculation of frequency estimated values and spatio-temporal consistency adjustment; S6: Drawing of heavy rain high-risk zoning maps. The method for heavy rain high-risk zoning based on hydrometeorological regional linear moment frequency analysis can not only obtain rainfall frequency estimated values with relatively high accuracy and precision, but also reflect the spatial distribution relationship among the three factors of "heavy rainfall intensity - heavy rain falling area - occurrence probability" to analyze heavy rain high-risk zoning, providing a scientific basis for engineering flood control design, flood control planning design for regions and cities, and early warning of short-duration mountain flood disasters. In the existing technical framework, methods for heavy rain high-risk zoning mostly rely on methods such as linear regression and statistical analysis. Although these methods can provide a certain degree of insight, they often neglect the spatial distribution characteristics and dynamic changes of hydrometeorological data. Especially in areas with large slopes, the changes in soil moisture and its impact on surface runoff are more complex, and traditional methods are difficult to effectively capture these subtle changes, thus affecting the accurate prediction and risk assessment of surface runoff. The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute prior art known to those of ordinary skill in the art. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for heavy rain high-risk zoning based on hydrometeorological spatial linear moment frequency analysis to solve the problems raised in the above background art.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for high-risk zoning of heavy rain based on hydrometeorological spatial linear moment frequency analysis, which is applied to a slope area with a slope height difference. The specific steps include: Step S1: According to the hydrometeorological spatial type distribution of the area to be monitored, divide all slope areas in the area to be monitored into several monitoring intervals according to the slope degree, and obtain the historical data of each monitoring interval in the area to be monitored to form a historical database. In each monitoring interval, mark multiple representative collection point groups that are closely related to the change of soil moisture, and each representative collection point group consists of a high-position collection point and a low-position collection point; Step S2: The historical database includes, but is not limited to, historical hydrometeorological data and historical surface runoff data in the previous monitoring time period before the current moment. Based on the historical database, determine the fluctuation intervals of the temperature value and rainfall in the area to be monitored; Use the linear moment frequency analysis method to process the historical hydrometeorological data of the current area to be monitored, calculate the extreme superposition values of the temperature value and rainfall respectively, combine the extreme superposition values and the fluctuation intervals to determine the upper limit interval of the superposition fluctuation, and use this upper limit interval of the fluctuation as the simulation value interval for subsequent rainfall simulation; Step S3: Construct a rainfall simulation model, use the temperature value, rainfall and surface runoff data in the previous monitoring time period in the historical database as inputs, and use the surface runoff data in the next monitoring time period as outputs to train the rainfall simulation model to obtain a rainfall simulation model with the temperature value and rainfall within the simulation interval range. The surface runoff data includes peak runoff and duration; Through the expert group system simulation or historical debris flow events, determine the risk threshold data set corresponding to the surface runoff prediction data of each monitoring interval; Step S4: Determine the real-time surface runoff data, as well as the temperature value and rainfall in the current meteorological environment, and input these determined data into the rainfall simulation model. The rainfall simulation model will output the surface runoff prediction data of each monitoring interval in the next monitoring time period; Step S5: Based on the surface runoff prediction data, collect the root growth characteristic data of all high-position collection points and low-position collection points in the representative collection point groups in each monitoring interval; Comprehensively analyze the root growth characteristic data of all representative collection point groups in each monitoring interval to generate an adjustment index for providing an adjustment strategy for the risk threshold data set corresponding to the surface runoff prediction data; Step S6: Compare and analyze the surface runoff prediction data of each monitoring interval with the risk threshold data set after the adjustment strategy, so as to conduct high-risk zoning of heavy rain for each monitoring interval in the area to be monitored.

[0005] A rainstorm high - risk zoning system based on hydrometeorological spatial linear moment frequency analysis, which is used to execute the rainstorm high - risk zoning method based on hydrometeorological spatial linear moment frequency analysis, includes: Division and marking module: It is used to divide all slope areas in the area to be monitored into several monitoring intervals according to the distribution of hydrometeorological spatial types in the area to be monitored, and obtain historical data of each monitoring interval in the area to be monitored to form a historical database. In each monitoring interval, multiple representative collection point groups closely related to soil moisture change are marked, and each representative collection point group consists of a high - level collection point and a low - level collection point; Simulation value range determination module: It is used to determine that the historical database includes, but is not limited to, hydrometeorological data and historical surface runoff data in the previous monitoring period before the current moment. Based on the historical database, the fluctuation ranges of the air temperature value and rainfall in the area to be monitored are determined; Based on the historical hydrometeorological data of the area to be monitored, use the linear moment frequency analysis method to process the historical hydrometeorological data of the current area to be monitored, respectively calculate the extreme superposition values of the air temperature value and rainfall, combine the extreme superposition value and the fluctuation range to determine the upper limit range of the superposed fluctuation, and use this upper limit range of the fluctuation as the simulation value range for subsequent rainfall simulation; Risk threshold data set determination module: It is used to construct a rainfall simulation model, take the air temperature value, rainfall and surface runoff data in the previous monitoring period in the historical database as inputs, and take the surface runoff data in the next monitoring period as outputs to train the rainfall simulation model, and obtain a rainfall simulation model in which the air temperature value and rainfall are within the simulation range. The surface runoff data includes peak runoff and duration; Through expert group system simulation or historical debris flow events, determine the risk threshold data set corresponding to the surface runoff prediction data of each monitoring interval; Prediction module: It is used to determine the real - time surface runoff data, air temperature value and rainfall in the current meteorological environment, and input these determined data into the rainfall simulation model. The rainfall simulation model will output the surface runoff prediction data of each monitoring interval in the next monitoring period; Adjustment index generation module: It is used to collect the root growth characteristic data of all high - level collection points and low - level collection points in the representative collection point group in each monitoring interval based on the surface runoff prediction data; Comprehensively analyze the root growth characteristic data of all representative collection point groups in each monitoring interval, and generate an adjustment index for providing an adjustment strategy for the risk threshold data set corresponding to the surface runoff prediction data; Zoning module: used to compare and analyze the surface runoff prediction data of each monitoring interval with the risk threshold data set adjusted by the strategy, so as to conduct heavy rain high-risk zoning for each monitoring interval in the area to be monitored.

[0006] Compared with the prior art, the beneficial effects of the present invention are as follows: by making a detailed slope division and setting representative sampling points in the area to be monitored, the rainfall simulation model can fully reflect the hydrological characteristics of the area, thereby improving the accuracy of surface runoff prediction; secondly, by combining historical hydrometeorological data and real-time meteorological conditions, the risk assessment can be dynamically adjusted to adapt to the changing climate environment and provide a more accurate flood risk warning; in addition, the comprehensive analysis of the root growth characteristic data in the method enhances the understanding of soil moisture changes, and thus in practical applications, it helps to formulate more effective disaster prevention and mitigation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a schematic diagram of the overall method flow of the present invention; Figure 2 is a block diagram of the system module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0008] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0009] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not represent any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object to be described changes, the relative positional relationship may also change accordingly.

[0010] Embodiment 1: Please refer to Figure 1 , the present invention provides a technical solution: A heavy rain high-risk zoning method based on hydrometeorological spatial linear moment frequency analysis, which is applied to a slope area with a slope height difference. This method can monitor and evaluate risks in real time during rainfall, so as to provide a basis for taking timely preventive measures. The specific steps include: Step S1: According to the hydrometeorological spatial type distribution of the area to be monitored, all slope areas in the area to be monitored are divided into several monitoring intervals according to the slope degree, and historical data of each monitoring interval in the area to be monitored are obtained to form a historical database. In each monitoring interval, multiple representative collection point groups closely related to soil moisture change are marked, and each representative collection point group consists of a high-position collection point and a low-position collection point; Step S2: Determine that the historical database includes but is not limited to hydrometeorological data and historical surface runoff data in the previous monitoring time period before the current moment. Based on the historical database, determine the fluctuation intervals of the air temperature value and rainfall in the area to be monitored; Based on the historical hydrometeorological data of the area to be monitored, use the linear moment frequency analysis method to process the historical hydrometeorological data of the current area to be monitored, calculate the extreme superposition values of the air temperature value and rainfall respectively, combine the extreme superposition values and the fluctuation intervals, determine the upper limit interval of the superposed fluctuation, and use this upper limit interval of the fluctuation as the simulation value interval for subsequent rainfall simulation; Step S3: Build a rainfall simulation model, use the air temperature value, rainfall and surface runoff data in the previous monitoring time period in the historical database as inputs, and use the surface runoff data in the next monitoring time period as outputs to train the rainfall simulation model, and obtain a rainfall simulation model with the air temperature value and rainfall within the simulation interval range. The surface runoff data includes peak runoff and duration; Through the expert group system simulation or historical debris flow events, determine the risk threshold data set corresponding to the surface runoff prediction data of each monitoring interval; Step S4: Determine the real-time surface runoff data, as well as the air temperature value and rainfall in the current meteorological environment, and input these determined data into the rainfall simulation model. The rainfall simulation model will output the surface runoff prediction data of each monitoring interval in the next monitoring time period; Step S5: Based on the surface runoff prediction data, collect the root growth characteristic data of all high-position collection points and low-position collection points in the representative collection point groups in each monitoring interval; Comprehensively analyze the root growth characteristic data of all representative collection point groups in each monitoring interval, and generate an adjustment index for providing an adjustment strategy for the risk threshold data set corresponding to the surface runoff prediction data; Step S6: Compare and analyze the surface runoff prediction data of each monitoring interval with the risk threshold data set after the adjustment strategy, so as to conduct a high-risk zoning of rainstorms for each monitoring interval in the area to be monitored.

[0011] Further explanation, the division of the monitoring interval and the marking of the representative collection point group specifically include: 1.1) Define the hydrometeorological spatial type distribution of the area to be monitored as follows: Using remote sensing data from satellite remote sensing technology, obtain surface vegetation cover, soil moisture, topographic features, and hydro-meteorological data of the area to be monitored. The hydro-meteorological data includes rainfall, temperature values, evaporation, and wind speed. Combine with on-site observation data from local meteorological stations, and collect hydro-meteorological data of the area to be monitored in the past 1 - 3 years to form a historical database; Spatial type analysis is as follows: Using Geographic Information System (GIS) software, in this embodiment, ArcGIS or QGIS is used to perform spatial type distribution analysis on remote sensing data and on-site observation data; According to topography, soil type, vegetation coverage, and hydro-response characteristics, divide all slope areas in the area to be monitored into the following monitoring intervals, specifically: High slope area: It represents an area with a slope above 70°. After precipitation, water loss is fast; Within 1 hour after precipitation, the water loss reaches 30 - 50 mm / h. This is because the slope is too large, resulting in precipitation being unable to penetrate and instead flowing away quickly; The soil water holding capacity is 15% - 20%.

[0012] Runoff coefficient: Relatively high, with a value range of 0.7 - 0.9, indicating that 70% - 90% of the precipitation is converted into runoff; Medium slope area: It represents an area with a slope range of (45°, 70°). After precipitation, water loss is average; The water loss rate is 15 - 30 mm / h. Water infiltrates upward in the soil, but still part of it will be lost through surface runoff; Soil water holding capacity: Keeps between 20% - 30%.

[0013] Runoff coefficient: Medium level, with a value range of 0.4 - 0.7, indicating that 40% - 70% of the precipitation is converted into runoff; Low slope area: It represents an area with a slope below 45°. After precipitation, water loss is small; The water loss rate after precipitation is 5 - 15 mm / h. Due to the gentle slope, most of the precipitation can penetrate into the soil, and the water loss speed is slow; Soil water holding capacity: In the range of 30% - 40%.

[0014] Runoff coefficient: The value range is 0.1 - 0.4, indicating that 10% - 40% of the precipitation is converted into runoff.

[0015] Record the high slope area, medium slope area, and low slope area as A, B, and C respectively, and set the numbers of the high slope area, medium slope area, and low slope area in the area to be monitored as N1, N2, and N3; For the high slope area, record any one high slope area as ; and ; represents the i1-th high slope area; For the middle slope area, any middle slope area is denoted as ; and ; represents the i2-th middle slope area; For the low slope area, any low slope area is denoted as ; and ; represents the i3-th low slope area; Visualize the classification results to generate a hydro-meteorological spatial type distribution map; The principles for dividing the monitoring intervals are as follows: Spatial uniformity principle: Ensure that the hydro-meteorological characteristics within each monitoring interval are relatively consistent; Moderate area principle: Ensure that the area difference between each monitoring interval is within 15% to facilitate data collection and analysis; Furthermore, mark the representative collection point groups that are closely related to soil moisture changes, including: Based on the historical database, first determine the surface runoff data of the i1-th high slope area , the i2-th middle slope area and the i3-th low slope area during each historical rainfall event, frame out the area including surface runoff as the selection interval for the representative collection point group, and the shortest distance from any point in the selection interval to the surface runoff does not exceed m1 meters; 2 ≤ m1 ≤ 5; The specific value of m1 is determined by the expert group system through experimental data and will not be elaborated; Set the high-level collection point of the representative collection point group at the highest point of the terrain in the selection interval; Set the low-level collection point at the lowest point of the terrain in the selection interval; Set the number of representative collection point groups in the i1-th high slope area as ; And any representative collection point group in the high slope area is denoted as ; represents the j-th representative collection point group in the high slope area , and ; Set the number of representative collection point groups in the i2-th middle slope area as ; And any representative collection point group in the middle slope area is denoted as ; represents the j-th representative collection point group in the middle slope area , and ; Set the i3rd low slope area The number of representative collection point groups is ; And for any one representative collection point group in the low slope area denote it as ; denotes the jth representative collection point group in the low slope area and ; In the corresponding historical rainfall events in the historical database, record the longitude and latitude, altitude, soil type, vegetation coverage of each representative collection point group, and the root growth characteristic data of the representative plant selected at this collection point group; The representative plant is a deep-rooted type plant that is sensitive to soil moisture changes. Among them, the root growth characteristic data includes root water absorption rate, root respiration rate, root growth rate, and root emergency response index; The definition of being sensitive to soil moisture changes is: when the change amount of soil moisture around the root system is 10%, will the root growth characteristic data change accordingly? The corresponding value is determined by the expert group through experimental data, and the corresponding value in this embodiment is set to 5%; For the root water absorption rate: The root water absorption rate refers to the rate at which the root system absorbs water from the soil during the current rainfall cycle, expressed in millimeters per hour (mm / h) or milligrams of water per gram of root per hour (mg water / g root / h); When the soil moisture is in a saturated state, the water absorption rate of the root system will decrease; Excessive water will cause root hypoxia or deterioration of the root environment; This will slow down the water absorption of the root system, thereby affecting the overall water use efficiency of the plant; For the root respiration rate: The root respiration rate refers to the rate at which the root system consumes oxygen and releases carbon dioxide during the current rainfall cycle in the physiological process; Expressed in milligrams of carbon dioxide per gram of root per hour (mg CO 2 / g root / h); Specifically, it is the mass of carbon dioxide consumed by each gram of root and released within one hour; The root respiration rate reflects the vitality and health status of the root system; In the case of saturated soil moisture, the root system faces an anoxic environment, resulting in a decrease in the respiration rate; This means that the metabolism of the root system is inhibited, and the plant shows signs of poor growth; Therefore, a low respiration rate is an important indicator of saturated or excessive soil moisture; For the root growth rate: The root growth rate refers to the change rate of the growth length of the root system relative to the time length of the current rainfall cycle; The growth rate of the root system is directly affected by soil moisture; under suitable moisture conditions, the root system will grow rapidly, while when the soil moisture is in a saturated state, the growth of the root system will be restricted, resulting in a reduced growth rate; a continuous high moisture state causes the root system to rot or grow abnormally, indicating that the soil moisture is in a saturated state; Regarding the root system emergency response indicators: The root system emergency response indicators refer to the concentration changes of signaling molecules produced by the root system during the current rainfall cycle when the soil moisture is in a saturated state. The signaling molecules include, but are not limited to, any one of abscisic acid and ethylene; These indicators reflect the stress response of the root system when the soil moisture is saturated or excessive; when the soil moisture is in a saturated state, it means that the plant will respond to root hypoxia and wilting problems by increasing the synthesis of stress-related signaling molecules; monitoring the changes in these emergency response indicators can help determine whether the soil moisture is saturated and whether the plant is coping with the challenge of excessive moisture.

[0016] Further explanation, the determination of the simulation value range specifically includes: Preprocess the hydrometeorological data collected from the historical database to remove outliers and missing values; Use the statistical analysis software Python for data cleaning to ensure the integrity of temperature and rainfall within each time period; For the cleaned temperature data, determine the minimum and maximum values of the temperature data from the historical database, denoted as and ; For the cleaned rainfall data, determine the minimum and maximum values of the rainfall from the historical database, denoted as and ; Set the fluctuation range of the temperature value as ; set the fluctuation range of the rainfall as ; Use the linear moment frequency analysis method to process the historical hydrometeorological data of the current area to be monitored, and perform extreme superposition calculations on the fluctuation ranges of temperature values and rainfall respectively, including: Based on the arithmetic mean and variance of the data, calculate the linear moment frequencies of temperature and rainfall respectively. The linear moment frequencies include: Linear moment frequency analysis is to calculate the mean and variance of temperature values and rainfall in the historical hydrometeorological data of the current area to be monitored; The first moment is the mean: describing the central tendency of temperature and rainfall; The second moment is the variance: describing the degree of dispersion of temperature and rainfall; The first moment is the arithmetic mean of the data, denoted by the symbol μ, which reflects the central tendency of the data and represents the average level of all observed values; the calculation formula is: ; where is the total number of data points, is the th observed value, and in this embodiment represents the air temperature value QW or the rainfall JY; The second moment is the variance of the data, denoted by the symbol , which measures the degree of dispersion of the data. The larger it is, the greater the difference between data points; the calculation formula is: ; The mean and variance of the air temperature values are respectively denoted as ; the mean and variance of the rainfall are respectively denoted as ; For the limit superposition calculation of the air temperature value: Set the corresponding upper limit of fluctuation of the air temperature value as , where is the limit superposition value of the air temperature value, is the corresponding upper limit of fluctuation of the air temperature value; For the limit superposition calculation of the rainfall: Set the corresponding upper limit of fluctuation of the rainfall as , where is the limit superposition value of the rainfall, is the corresponding upper limit of fluctuation of the rainfall; Through the calculation of the limit superposition value, higher upper limits of fluctuation can be set for the air temperature value and the rainfall respectively, reflecting the possible changes under extreme meteorological conditions and improving the accuracy of risk assessment; According to the determined upper limit interval of the air temperature value fluctuation and the upper limit interval of the rainfall fluctuation , the rainfall conditions are simulated; And are used as the simulation value intervals for rainfall simulation.

[0017] Further explanation, determining the risk threshold data set corresponding to the surface runoff prediction data of each monitoring interval specifically includes: The rainfall simulation model uses a numerical weather model to determine the real-time surface runoff data of each current monitoring interval. Taking the real-time surface runoff data and any combination of the air temperature value and the rainfall as inputs, multiple rainfall simulations are carried out through the numerical weather model, so as to obtain the output results of the surface runoff prediction data of each monitoring interval; in this embodiment, the numerical weather model uses the Weather Research and Forecasting Model model, Input the topographic, soil type, and vegetation cover data of each monitoring area into the numerical meteorological model to ensure that the numerical meteorological model can accurately reflect the local hydrological characteristics; By actually simulating surface runoff under different conditions, reliable hydrological response data can be obtained to support subsequent risk assessments; Set the surface runoff prediction data to include the following: Peak runoff: The highest surface runoff value observed during the simulation in each monitoring interval; Average runoff: The average surface runoff value calculated during the simulation in each monitoring interval; Duration: Set the upper threshold value of the surface runoff value, and define the duration of runoff above the upper threshold value as the duration; The determination strategy of the risk threshold data set includes: Form an evaluation group consisting of experts in the fields of hydrology, meteorology, and debris flow, hold regular meetings, and conduct analyses based on simulation results and historical data; Collection of historical debris flow event data: Collect data on historical debris flow events, including information such as the occurrence time, location, meteorological conditions, surface runoff data, and the affected range of the debris flow; Compare and analyze the surface runoff prediction data obtained from the simulation with the historical debris flow event data to determine the risk threshold data set for each monitoring interval, specifically including: Record any combination of air temperature values and rainfall amounts as , Belonging to the upper limit interval of air temperature value fluctuations ; Belonging to the upper limit interval of rainfall fluctuations ; z1 and z2 are the numerical indices of the air temperature value and rainfall amount in the corresponding upper limit intervals of fluctuations, respectively; Collect the surface runoff data for each monitoring interval under any combination from the rainfall simulation model, including the following indicators: Denote the high slope area , the middle slope area and the low slope area collectively as X, and obtain X ∈ { , , }; Denote the peak runoff of the monitoring interval X under the combination as ; Denote the average runoff of the monitoring interval X under the combination as ; Denote the monitoring interval X under the combination The duration under is recorded as ; Collect the data of debris flow events that occurred in each past monitoring interval to respectively set the peak runoff and the duration risk thresholds, and the risk thresholds are respectively divided into high-risk level thresholds, medium-risk level thresholds, and low-risk level thresholds according to the values from large to small; and organize these risk thresholds into a risk threshold dataset; including the following information: Event occurrence date, event location, event scale (such as affected area, debris flow volume, etc.), meteorological conditions before the event, and the meteorological conditions are rainfall and temperature values; Set the risk thresholds of the peak runoff by the expert group system according to the historical debris flow occurrence situation as L1 and L2 respectively: For the high-risk level threshold situation: ; In this embodiment, L1 = ; For the medium-risk level threshold situation: ; In this embodiment, L2 = ; For the low-risk level threshold situation: ; Set the corresponding risk thresholds of the duration as L3 and L4 respectively: For the high-risk level threshold situation: hours; In this embodiment, L3 = ; For the medium-risk level threshold situation: ; In this embodiment, L4 = ; For the low-risk level threshold situation: .

[0018] Further illustrate that the combination of the temperature value and the rainfall amount in the current meteorological environment is set as ; and are respectively the numerical indices of the current temperature value and the rainfall amount in the corresponding upper fluctuation intervals; Record the peak runoff in the surface runoff prediction data of each monitoring interval as , and record the duration as ; And determine the risk thresholds of the peak runoff in the risk threshold dataset as L1 and L2 respectively, and determine the risk thresholds of the duration as L3 and L4 respectively; In the j-th representative sampling point group within the monitoring interval X, the root growth characteristic data of the high-level sampling point corresponding to the j-th representative sampling point group is expressed as ; where respectively represent the root water absorption rate, root respiration rate, root growth rate, and root emergency response index of the high-level sampling point corresponding to the j-th representative sampling point group within the monitoring interval X, and gw is the abbreviated label of the high-level sampling point; In the j-th representative sampling point group within the monitoring interval X, the root growth characteristic data of the low-level sampling point corresponding to the j-th representative sampling point group is expressed as ; where respectively represent the root water absorption rate, root respiration rate, root growth rate, and root emergency response index of the low-level sampling point corresponding to the j-th representative sampling point group within the monitoring interval X; and dw is the abbreviated label of the low-level sampling point; Define the adjustment index of the monitoring zone X under the current air temperature value and rainfall combination as , and the calculation formula is as follows: ; where R1, R2, R3, and R4 are the average difference variation coefficients of the root water absorption rate, root respiration rate, root growth rate, and root emergency response index for all high-level and low-level sampling points within the corresponding monitoring interval X, respectively; Perform standardization processing on R1, R2, R3, and R4 so that the standardized output value is adjusted to the range (0, 1) through scaling and offset to obtain ; Set the valid value range of the adjustment index to (0, 1); Based on experimental demonstration or expert group system analysis, determine that the adjustment threshold range of the adjustment index is Q1, and 0.3 ≤ Q1 ≤ 0.72; the value range of Q1 is adjusted according to the expert group system through experimental data and is not limited; If takes values in (0, 0.3), approaches 0, indicating that the current soil moisture is in a saturated state, and the adjustment strategy for the risk threshold dataset is as follows: ; If takes values in (0.72, 1), approaches 1, indicating that the current soil moisture is not in a saturated state, and the adjustment strategy for the risk threshold dataset is as follows: ; where They are the corresponding risk thresholds after adjustment respectively.

[0019] If When the value is in the range of [0.3, 0.72], no adjustment is made to the risk threshold dataset.

[0020] For further illustration, a rainstorm high-risk zoning is carried out for each monitoring interval in the area to be monitored, specifically including: Based on the current real-time surface runoff data, according to the temperature values and rainfall amounts of each monitoring interval in the current area to be monitored, the combinations of temperature values and rainfall amounts are determined, and the peak runoff output by the numerical meteorological model and the duration are respectively compared and analyzed with the corresponding risk thresholds in the risk threshold dataset after the adjustment strategy, and the most serious risk level in the comparison and analysis results is used as the rainstorm high-risk zoning of each current monitoring interval.

[0021] Embodiment 2: Please refer to Figure 2 , a rainstorm high-risk zoning system based on hydrometeorological spatial linear moment frequency analysis, the system is used to execute the rainstorm high-risk zoning method based on hydrometeorological spatial linear moment frequency analysis, including: Division and marking module: used to divide all slope areas in the area to be monitored into several monitoring intervals according to the distribution of hydrometeorological spatial types in the area to be monitored, and obtain the historical data of each monitoring interval in the area to be monitored to form a historical database. In each monitoring interval, multiple representative collection point groups closely related to soil moisture change are marked, and each representative collection point group consists of a high-position collection point and a low-position collection point; Simulation value range determination module: used to determine that the historical database includes but is not limited to the hydrometeorological data and historical surface runoff data of the previous monitoring period before the current moment, and based on the historical database, to determine the fluctuation range of the temperature value and rainfall amount in the area to be monitored; Based on the historical hydrometeorological data of the area to be monitored, the historical hydrometeorological data of the current area to be monitored is processed by using the linear moment frequency analysis method to calculate the extreme superposition values of the temperature value and rainfall amount respectively. Combining the extreme superposition value and the fluctuation range, the upper limit range of the superposed fluctuation is determined, and this upper limit range of the fluctuation is used as the simulation value range for subsequent rainfall simulation; Risk threshold dataset determination module: used to construct a rainfall simulation model, take the temperature value, rainfall amount and surface runoff data of the previous monitoring period in the historical database as inputs, and take the surface runoff data of the next monitoring period as outputs to train the rainfall simulation model, and obtain a rainfall simulation model in which the temperature value and rainfall amount are within the simulation range. The surface runoff data includes peak runoff and duration; Determine the risk threshold data set corresponding to the predicted surface runoff data for each monitoring interval through expert group system simulation or historical debris flow events; Prediction module: used to determine the real-time surface runoff data, air temperature value and rainfall amount under the current meteorological environment, input the determined data into the rainfall simulation model, and the rainfall simulation model will output the predicted surface runoff data for each monitoring interval in the next monitoring time period; Adjustment index generation module: used to collect the root growth characteristic data of all high-position collection points and low-position collection points in the representative collection point group in each monitoring interval based on the predicted surface runoff data; Comprehensively analyze the root growth characteristic data of all representative collection point groups in each monitoring interval, and generate an adjustment index for providing an adjustment strategy for the risk threshold data set corresponding to the predicted surface runoff data; Zoning module: used to compare and analyze the predicted surface runoff data of each monitoring interval with the risk threshold data set after the adjustment strategy, so as to conduct heavy rain high-risk zoning for each monitoring interval in the area to be monitored.

[0022] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to get the closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0023] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0024] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0025] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A method for high-risk zoning for rainstorms based on hydro-meteorological spatial linear moment frequency analysis, which is applied to slope areas with slope height differences, is characterized by: The specific steps include: Step S1: According to the hydrological and meteorological spatial type distribution of the area to be monitored, all slope areas in the area to be monitored are divided into several monitoring intervals according to the slope degree, and the historical data of each monitoring interval in the area to be monitored is obtained to form a historical database. In each monitoring interval, multiple representative collection point groups closely related to soil moisture changes are marked, and each representative collection point group consists of a high-level collection point and a low-level collection point; Step S2: The historical database includes but is not limited to the historical hydrological and meteorological data and historical surface runoff data of the monitoring period before the current moment, and the fluctuation range of the temperature value and rainfall in the monitored area is determined based on the historical database; The historical hydrological and meteorological data of the current monitored area are processed using the linear moment frequency analysis method to calculate the extreme superposition values ​​of temperature and rainfall respectively. The upper limit interval of fluctuation after superposition is determined by combining the extreme superposition value and the fluctuation range, and the upper limit interval of fluctuation is used as the simulation value interval for subsequent rainfall simulation. Step S3: construct a rainfall simulation model, take the temperature value, rainfall and surface runoff data of the previous monitoring time period in the historical database as input, and take the surface runoff data of the next monitoring time period as output, so as to train the rainfall simulation model, and obtain a rainfall simulation model whose temperature value and rainfall are within the simulation interval, and the surface runoff data include peak runoff and duration; Determine the risk threshold data set corresponding to the surface runoff prediction data of each monitoring interval through the expert group system simulation or historical debris flow events; Step S4: determining the real-time surface runoff data, temperature value and rainfall under the current meteorological environment, and inputting the determined data into the rainfall simulation model, which will output the surface runoff prediction data of each monitoring interval in the next monitoring time period; Step S5: Based on the surface runoff prediction data, the root growth characteristic data of all high-position collection points and low-position collection points in the representative collection point group in each monitoring interval are collected; Comprehensively analyze the root growth characteristic data of all representative collection point groups in each monitoring interval to generate an adjustment index for providing an adjustment strategy for the risk threshold data set corresponding to the surface runoff prediction data; Step S6: Compare and analyze the surface runoff prediction data of each monitoring interval with the risk threshold data set after the adjustment strategy, so as to divide each monitoring interval in the monitoring area into high-risk zoning for heavy rain.

2. The method for high-risk zoning of rainstorms based on hydro-meteorological spatial linear moment frequency analysis according to claim 1 is characterized by: The division of monitoring intervals includes: Use geographic information system software such as ArcGIS or QGIS to analyze the spatial type distribution of remote sensing data and field observation data; According to the results of spatial type distribution analysis, all slope areas in the area to be monitored are divided into the following monitoring intervals: High slope area: refers to areas with slopes above 70°, where water loss is fast after precipitation; Medium slope area: refers to the area with a slope range of (45°, 70°), where water loss after precipitation is average; Low slope area: refers to the area with a slope of less than 45°, where there is less water loss after precipitation; The high slope area, the middle slope area and the low slope area are recorded as A, B and C respectively, and the number of the high slope area, the middle slope area and the low slope area in the area to be monitored is set as N1, N2 and N3 respectively; For high slope areas, any high slope area is recorded as ;and ; represents the i1th high slope area; For the mid-slope area, any mid-slope area is recorded as ;and ; represents the i2th mid-slope area; For low slope areas, any low slope area is recorded as ;and ; Indicates the i3th low slope area.

3. The method for high-risk zoning of rainstorms based on hydrological and meteorological spatial linear moment frequency analysis according to claim 2 is characterized by: The representative collection point groups closely related to soil moisture changes are marked, including: Based on the historical database, first determine the i1th high slope area , the second middle slope area and the third low slope area In the surface runoff data of each historical rainfall event, the area including the surface runoff is selected as the selection interval of the representative collection point group, and the shortest distance between any point in the selection interval and the surface runoff is no more than m1 meter; The high-position collection point representing the collection point group is set at the highest point of the terrain in the selected interval; the low-position collection point is set at the lowest point of the terrain in the selected interval; Set the i1th high slope area The number of representative collection point groups is ; and the high slope area Any one of the representative collection point groups is recorded as ; Indicates high slope area The jth one in represents the collection point group, and ; Set the i2nd mid-slope area The number of representative collection point groups is ; and the Zhongpo area Any one of the representative collection point groups is recorded as ; Indicates the middle slope area The jth one in represents the collection point group, and ; Set the i3rd low slope area The number of representative collection point groups is ; and the low-slope areas Any one of the representative collection point groups is recorded as ; Indicates low slope area The jth one in represents the collection point group, and ; In the historical rainfall events corresponding to the historical database, the longitude and latitude, altitude, soil type, vegetation coverage of each representative collection point group are recorded, and the root growth characteristic data of the representative plants at the collection point group are selected; The representative plant is a deep-rooted plant that is sensitive to soil moisture changes. The root growth characteristic data include root water absorption rate, root respiration rate, root growth rate and root emergency response index; The root water uptake rate refers to the rate at which the roots absorb water from the soil during the current rainfall cycle; Root respiration rate refers to the rate at which the roots consume oxygen and release carbon dioxide in physiological processes during the current rainfall cycle; The root growth rate refers to the ratio of the root growth length in the current rainfall cycle to the duration of the cycle; The root emergency response index refers to the change in concentration of signal molecules produced by the root system when the soil moisture is in a saturated state during the current rainfall cycle. The signal molecules include but are not limited to abscisic acid.

4. The method for high-risk zoning of rainstorms based on hydrological and meteorological spatial linear moment frequency analysis according to claim 3 is characterized by: The determination of the simulation value range includes: Determine the minimum and maximum values ​​of the temperature data from the historical database and record them as and , and calculate the mean of the temperature values and variance ; Determine the minimum and maximum rainfall values ​​from the historical database and record them as and , and calculate the mean rainfall and variance ; Set the fluctuation range of the temperature value to ; Set the fluctuation range of rainfall to ; Maximum value based on air temperature , mean and variance , calculate the limit superposition value of the temperature value and the corresponding fluctuation upper limit, the calculation formula is as follows: ； in, is the limit superposition value of the temperature value, is the corresponding fluctuation upper limit of the temperature value; The upper limit of the temperature fluctuation range is further determined as ; Maximum value based on rainfall , mean and variance , calculate the limit superposition value of rainfall and the corresponding fluctuation upper limit, the calculation formula is as follows: ； in, is the limit superposition value of rainfall, is the corresponding fluctuation upper limit of rainfall; The upper limit of rainfall fluctuation is further determined as ; Will and As the simulation value range for rainfall simulation.

5. The method for high-risk zoning of rainstorms based on hydro-meteorological spatial linear moment frequency analysis according to claim 4 is characterized by: Determine the risk threshold data set corresponding to the surface runoff prediction data of each monitoring interval, including: The rainfall simulation model uses a numerical meteorological model to determine the real-time surface runoff data of each monitoring interval. The real-time surface runoff data and any combination of temperature and rainfall are used as inputs. Multiple rainfall simulations are performed through the numerical meteorological model to obtain the output results of the surface runoff prediction data of each monitoring interval. The surface runoff prediction data are set to include the following: Peak runoff: the highest surface runoff value observed during the simulation in each monitoring interval; Duration: Set the upper threshold of surface runoff value, and define the duration of runoff above the upper threshold as duration; the strategy for determining the risk threshold dataset includes: The simulated surface runoff prediction data was compared with the historical debris flow event data to determine the risk threshold data set for each monitoring interval, including: Any combination of temperature and rainfall is recorded as , Belongs to the upper limit of temperature fluctuation range ; Belongs to the upper limit of rainfall fluctuation range ; z1 and z2 are the numerical indexes of the temperature and rainfall in the corresponding upper fluctuation ranges; Based on the current real-time surface runoff data, the rainfall simulation model collects the data for each monitoring interval in any combination. The surface runoff data includes the following indicators: The high slope area , Zhongpo District and low slope areas Unified as X, we get X∈{ , , }; The monitoring interval X is combined The peak runoff under ; The monitoring interval X is combined The duration of ; Collect the data of debris flow events that occurred in each monitoring interval in the past to set the peak runoff respectively and duration The risk thresholds are divided into high risk level thresholds, medium risk level thresholds and low risk level thresholds according to the values ​​from large to small; and these risk thresholds are organized into a risk threshold data set; The peak runoff is set based on historical debris flow occurrences through an expert group system The risk thresholds are L1 and L2 respectively: For high risk level threshold situations: ; For medium risk threshold situations: ; For low risk level threshold situations: ; Set the corresponding duration The risk thresholds are L3 and L4 respectively: For high risk level threshold situations: Hour; For medium risk threshold situations: ; For low risk level threshold situations: .

6. The method for high-risk zoning of rainstorms based on hydrological and meteorological spatial linear moment frequency analysis according to claim 5 is characterized by: Set the temperature and rainfall combination under the current meteorological environment to ; and They are the numerical indexes of the current temperature and rainfall in the corresponding upper fluctuation ranges; The peak runoff of each monitoring interval in the surface runoff prediction data is recorded as , and record the duration as ; and determine peak runoff in the risk threshold dataset The risk thresholds are L1 and L2, and the duration is determined The risk thresholds are L3 and L4 respectively; In the jth representative collection point group within the monitoring interval X, the root growth characteristic data of the high-position collection point corresponding to the jth representative collection point group is expressed as ; in They represent the root water absorption rate, root respiration rate, root growth rate and root emergency response index of the corresponding high-level collection point in the jth representative collection point group within the monitoring interval X, and gw is the abbreviation of the high-level collection point; In the jth representative collection point group within the monitoring interval X, the root growth characteristic data of the low-level collection point corresponding to the jth representative collection point group is expressed as ; in They represent the root water absorption rate, root respiration rate, root growth rate and root emergency response index of the corresponding low-level collection point in the jth representative collection point group within the monitoring interval X; and dw is the abbreviation of the low-level collection point; Based on the current real-time surface runoff data, define the monitoring zone X in the current temperature value and rainfall combination The adjustment index is , the calculation formula is as follows: ； Among them, R1, R2, R3, and R4 are the average difference coefficients of root water absorption rate, root respiration rate, root growth rate, and root emergency response index at all high-level and low-level collection points in the corresponding monitoring interval X, respectively; R1, R2, R3, and R4 are standardized so that the standardized output value is adjusted to the range of (0, 1) by scaling and offsetting to obtain ; Setting the Adjustment Index The valid value range is (0,1).

7. The method for high-risk zoning of rainstorms based on hydro-meteorological spatial linear moment frequency analysis according to claim 6 is characterized by: Determine the adjustment index based on experimental demonstration or expert group system analysis The adjustment threshold range is Q1, and 0.3≤Q1≤0.72; like When the value is (0,0.3), Approaching 0 means that the current soil moisture is in a saturated state. The adjustment strategy of the risk threshold dataset is as follows: ； like When the value is (0.72,1), Approaching 1 means that the current soil moisture is not saturated. The adjustment strategy of the risk threshold dataset is as follows: ； in, are the corresponding risk thresholds after adjustment; like When the value is [0.3, 0.72], no adjustment is made for the risk threshold dataset.

8. The method for high-risk zoning of rainstorms based on hydrological and meteorological spatial linear moment frequency analysis according to claim 7 is characterized by: Each monitoring interval in the monitoring area is divided into high-risk areas for heavy rain, including: Based on the current real-time surface runoff data, the peak runoff output by the numerical meteorological model is converted into the peak runoff value according to the combination of temperature value and rainfall in each monitoring interval in the current monitoring area. With duration The risk thresholds corresponding to the risk threshold data set after the adjustment strategy are compared and analyzed respectively, and the most serious risk level in the comparison and analysis results is used as the high-risk zoning for heavy rain in each current monitoring interval.

9. A rainstorm high-risk zoning system based on hydrological and meteorological spatial linear moment frequency analysis, characterized by: The system is used to execute the method for high-risk zoning of rainstorms based on hydrological and meteorological spatial linear moment frequency analysis according to any one of claims 1 to 8, comprising: Division and marking module: used to divide all slope areas in the monitored area into several monitoring intervals according to the slope degree according to the distribution of hydrological and meteorological spatial types in the monitored area, and obtain the historical data of each monitoring interval in the monitored area to form a historical database. In each monitoring interval, multiple representative collection point groups closely related to soil moisture changes are marked, and each representative collection point group consists of a high-level collection point and a low-level collection point. Simulation value interval determination module: used to determine the historical database including but not limited to the hydrological and meteorological data and historical surface runoff data of the monitoring period before the current moment, based on the historical database, to determine the fluctuation range of the temperature value and rainfall in the monitored area, including the fluctuation range of the temperature value and rainfall; Based on the historical hydrological and meteorological data of the area to be monitored, the linear moment frequency analysis method is used to process the historical hydrological and meteorological data of the current area to be monitored, so as to calculate the limit superposition values ​​of the temperature value and the rainfall respectively, and the upper limit interval of the fluctuation after superposition is determined by combining the limit superposition value and the fluctuation range, and the upper limit interval of the fluctuation is used as the simulation value interval of the subsequent rainfall simulation; Risk threshold data set determination module: used to build a rainfall simulation model, taking the temperature value, rainfall and surface runoff data of the previous monitoring period in the historical database as input, and taking the surface runoff data of the next monitoring period as output, so as to train the rainfall simulation model and obtain a rainfall simulation model whose temperature value and rainfall are within the simulation interval. The surface runoff data includes peak runoff and duration; Determine the risk threshold data set corresponding to the surface runoff prediction data of each monitoring interval through the expert group system simulation or historical debris flow events; Prediction module: used to determine the real-time surface runoff data, temperature value and rainfall under the current meteorological environment, and input these determined data into the rainfall simulation model, which will output the surface runoff prediction data for each monitoring interval in the next monitoring period; Adjustment index generation module: used to collect root growth characteristic data of all high-position collection points and low-position collection points in the representative collection point group within each monitoring interval based on the surface runoff prediction data; Comprehensively analyze the root growth characteristic data of all representative collection point groups in each monitoring interval to generate an adjustment index for providing an adjustment strategy for the risk threshold data set corresponding to the surface runoff prediction data; Zoning module: It is used to compare and analyze the surface runoff prediction data of each monitoring interval with the risk threshold data set after adjusting the strategy, so as to carry out heavy rain high risk zoning for each monitoring interval in the monitoring area.

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