Mountainous frost disaster risk precise zoning method considering slope aspect correction

Abstract

This invention discloses a precise zoning method for mountainous freezing disaster risk considering slope and aspect correction, belonging to the field of natural disaster risk assessment technology. This method quantitatively introduces the microphysical regulation effect of topography on freezing maintenance and melting into the hazard assessment process, constructs a disaster hazard actuarial system from the identification of freezing climate days, the quantification of annual freezing intensity index to the correction of joint slope and aspect correction coefficients, and organically couples it with the exposure index and vulnerability index of the disaster-bearing body to obtain the freezing disaster risk index and classify the level. It incorporates fine topographic factors into the freezing hazard correction process, and reconstructs the spatial distribution of hazard by grid point through differential assignment of shady slope group and sunny slope group. This changes the uniformity defect of traditional zoning in complex mountainous areas, significantly improves the accuracy and spatial precision of risk zoning, and provides reliable technical support for the precise prevention and control of freezing disasters in mountainous areas.

Description

Technical Field

[0001] This invention relates to the field of natural disaster risk assessment technology, specifically to a method for accurate zoning of mountain freezing disaster risks that takes into account slope and aspect correction. Background Technology

[0002] Freezing disasters mainly include rime ice, hoarfrost, and mixed ice accumulation. They are common meteorological disasters in mountainous areas during the winter months, often causing power transmission line breaks and tower collapses, road icing disruptions, tree breakage, and damage to communication facilities. They pose a serious threat to the socio-economic operation of mountainous areas. Conducting freezing disaster risk zoning is an important foundation for optimizing disaster prevention resource allocation and guiding the ice-resistant design of engineering projects.

[0003] Existing methods for zoning freezing disaster risks mostly rely on meteorological station observation data, such as the number of days with freezing rain, the number of days with freezing, and calculated ice thickness. These data are then used to generate a risk distribution of hazard-causing factors through spatial interpolation, and finally superimposed with the exposure and vulnerability of the affected body to obtain a risk zoning map. However, in mountainous terrain, the formation, maintenance, and melting of ice are significantly controlled by local topography. On the one hand, the slope directly affects the stability of ice adhesion and the growth pattern of icicles; ice layers on steep slopes are more prone to detachment and are relatively less dangerous. On the other hand, slope aspect affects the amount of solar radiation received, determining the melting rate of the ice layer and the duration of freezing. Shady slopes, due to less radiation intake, experience longer ice accumulation and are significantly more dangerous than sunny slopes at the same altitude. The current zoning method does not systematically correct for the differences caused by slope and aspect, resulting in insufficient spatial accuracy of the ice disaster risk zoning map obtained in complex mountainous areas. It cannot truly reflect the risk differentiation of different terrain parts under the same climatic background, and may cause high-risk areas to be missed or low-risk areas to be overestimated, thus affecting the effectiveness and pertinence of ice-fighting decisions.

[0004] Therefore, there is an urgent need for a precise zoning method for mountainous freezing disaster risks that can accurately depict the control effect of terrain and incorporate slope and aspect into the correction process. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a precise risk zoning method for freezing disasters in mountainous areas that considers slope and aspect correction. This method quantitatively introduces the microphysical regulation effect of topography on freezing maintenance and melting into the risk assessment process. It constructs a disaster risk actuarial system from the identification of freezing climate days and the quantification of annual freezing intensity indicators to the correction of joint slope and aspect correction coefficients. It is organically coupled with the exposure index and vulnerability index of the disaster-bearing body to obtain a freezing disaster risk index and classify levels. It incorporates fine topographic factors into the freezing risk correction process and reconstructs the spatial distribution of risks by assigning differential values ​​to shady slope groups and sunny slope groups on a grid-by-grid basis. This method changes the uniformity defect of traditional zoning in complex mountainous areas, significantly improves the accuracy and spatial precision of risk zoning, and provides reliable technical support for the precise prevention and control of freezing disasters in mountainous areas.

[0006] To solve the above-mentioned technical problems, this invention provides the following technical solution: a method for accurate zoning of mountainous freezing disaster risks considering slope and aspect correction, the specific steps of which are as follows: S100. Obtain basic data for the study area, including daily observation data from meteorological stations, digital elevation model data, data on the distribution of disaster-bearing bodies, and historical records of freezing disasters. S200. Based on the daily observation data of the meteorological stations, calculate the basic index of freezing disaster risk at each spatial grid point in the study area; S300. Using the digital elevation model data, extract the spatial distribution of slope and aspect of the study area, and construct the joint correction coefficient of slope and aspect. S400. The basic index of frost disaster risk is corrected using the slope and aspect joint correction coefficient to obtain the corrected frost disaster risk index. S500. Based on the disaster-bearing body distribution data, calculate the disaster-bearing body exposure index and the disaster-bearing body vulnerability index; S600. Calculate the freezing disaster risk index based on the corrected freezing disaster hazard index, the disaster-bearing body exposure index, and the disaster-bearing body vulnerability index, and classify the risk level based on the freezing disaster risk index to generate a precise zoning map of freezing disaster risk in mountainous areas.

[0007] Furthermore, in S100, the daily observation data of the meteorological station includes daily minimum temperature, daily precipitation, daily average relative humidity, and daily average wind speed; The disaster-bearing body distribution data includes population density distribution data, road network distribution data, power line distribution data, and land use and land cover data.

[0008] Furthermore, in S200, the calculation steps for the basic index of freezing-induced disaster risk include: For each meteorological station, freezing weather days are determined using daily minimum temperature, daily precipitation, and daily average relative humidity: a freezing weather day is defined when the daily minimum temperature is ≤0℃, the daily precipitation is ≥1.0 mm, and the daily average relative humidity is ≥80%. For each meteorological station, calculate the annual freezing intensity index for each station within each year, focusing only on all identified freezing weather days. ,in, For the first Daily precipitation on a freezing weather day For the first The daily average wind speed on a freezing weather day and These are the empirical weighting coefficients for precipitation and the wind speed weighting coefficient, respectively. Based on the annual freezing intensity index of various meteorological stations over many years The sequence is spatially interpolated to generate a raster of spatial distribution of multi-year average freezing intensity. Using the historical records of freezing disasters, a mapping relationship is established between different freezing intensity ranges and the probability of disaster. Based on the mapping relationship, the spatial distribution grid of the multi-year average freezing intensity is normalized to obtain a basic index of freezing disaster risk with a value range of 0-1.

[0009] Furthermore, in S300, the construction of the joint correction coefficient for slope and aspect includes: Using digital elevation model data, the slope angle of each grid point is calculated at a predetermined target spatial resolution. and slope angle ,in With true north as 0°, the angle increases clockwise. Slope angle The slope is divided into four grades: flat, gentle, sloping, and steep, with the slope aspect angle... Based on the intensity of solar radiation received, the slopes are divided into three groups: shady slope, transitional slope, and sunny slope. A correction factor value is pre-set for each combination of slope grade and aspect group, forming a joint correction coefficient assignment table for slope and aspect. For each grid point, the slope correction factor corresponding to the slope grade is obtained. and the slope aspect correction factor corresponding to the slope aspect group. And calculate the joint slope and aspect correction factor for each grid point. ,in, The values ​​are extracted from the slope and aspect joint correction coefficient assignment table based on the grid slope level. The values ​​are extracted from the slope and aspect joint correction coefficient assignment table based on the grid point aspect group.

[0010] Furthermore, the slope grade classification standard is as follows: flatness corresponds to a slope angle. <5°, gentle slope corresponds to a slope angle of 5°≤ <15°, the slope angle corresponding to a slope of 15°≤ <25°, steep slope corresponding to slope angle ≥25°; The slope aspect grouping standard is: grouping slope angles... The north slopes (0°-45°, 315°-360°), northeast slopes (45°-90°), and northwest slopes (270°-315°) are classified as shaded slopes, and the slope aspect angles are... The south, southeast, and southwest slopes with angles between 135° and 225° are classified as the sunny slope group; the remaining slope aspects are... It was designated as a transitional group; In the joint correction factor assignment table for slope and aspect, the correction factor value corresponding to the shady slope group is >1, and the correction factor value corresponding to the sunny slope group is <1. Furthermore, for the same aspect group, as the slope grade changes from flat to steep, the correction factor value shows a decreasing trend.

[0011] Furthermore, in S400, the corrected frost-induced disaster risk index is obtained by: acquiring the basic frost-induced disaster risk index and the slope-aspect joint correction coefficient, and then... and the combined slope and aspect correction factor ,according to The corrected freezing disaster risk index is calculated point by point. For the calculated The raster is recalibrated to remap the numerical range to 0-1.

[0012] Furthermore, in S500, the calculation process for the disaster-bearing body exposure index is as follows: Extract population density distribution data, road network distribution data, and power line distribution data from the disaster-bearing body distribution data, and generate population density raster, road network density raster, and power line density raster respectively. The population density grid, road network density grid, and power line density grid are normalized to 0-1 respectively to obtain normalized population density, normalized road network density, and normalized power line density. The normalized population density, normalized road network density, and normalized power line density are combined using a weighted summation method, and the combined result is normalized again to 0-1 to obtain the disaster exposure index E.

[0013] Furthermore, in S500, the vulnerability index of the disaster-bearing body is calculated as follows: using the land use and land cover data in the disaster-bearing body distribution data, based on the degree of loss of different land cover types in historical freezing disasters, a vulnerability weight is preset, and a vulnerability value is directly assigned to each spatial grid point to form a vulnerability index V of the disaster-bearing body with a value range between 0 and 1.

[0014] Furthermore, in S600, the calculation of the freezing disaster risk index and the classification of risk levels include: Obtain the corrected freezing disaster risk index, the disaster-bearing body exposure index, and the disaster-bearing body vulnerability index, and record the corrected freezing disaster risk index as... The exposure index of the disaster-bearing body and the vulnerability index of the disaster-bearing body ,according to Calculate the risk index of freezing disasters Based on the calculated freezing disaster risk index The value range distribution of the freezing disaster risk index The area was designated as a low-risk zone, and the freezing disaster risk index was adjusted. The area was designated as a medium-risk area, and the freezing disaster risk index was increased. The area was designated as a high-risk zone, and the freezing disaster risk index was increased. The area has been designated as an extremely high-risk zone.

[0015] Compared with existing technologies, this method for precise zoning of mountain freezing disaster risks, which considers slope and aspect correction, has the following advantages: I. This invention quantitatively introduces the microphysical regulation effect of topography on freezing maintenance and melting into the hazard assessment process. Based on the slope angle of each grid point, slopes are divided into flat, gentle, sloping, and steep slopes. The slope aspect angle is further classified into shady slope group, transition group, and sunny slope group. Differentiated correction factors are assigned using an assignment table to correct the basic index of freezing hazard risk point by grid point. This processing method highlights high-risk micro-topography such as shady slopes and gentle slopes, while reasonably weakening low-risk areas such as sunny slopes and steep slopes. It overcomes the homogenization of mountain freezing hazard caused by relying solely on spatial interpolation of meteorological data, significantly improves the spatial resolution and micro-topography characterization accuracy of the final risk zoning map, and effectively reduces the identification errors of omitting high-risk areas or overestimating low-risk areas.

[0016] Second, this invention constructs a disaster risk assessment logic chain of hazard, exposure, and vulnerability. It determines freezing weather days based on daily minimum temperature, daily precipitation, and daily average relative humidity, and uses the annual freezing intensity index to obtain a basic index of freezing disaster hazard through multi-year average and disaster probability mapping. Then, it uses a slope and aspect joint correction coefficient to ensure the physical authenticity of the spatial expression of hazard. It obtains the exposure index of disaster-bearing body by comprehensively normalizing population density, road network density, and power line density, and assigns a vulnerability index of disaster-bearing body based on land use and land cover type. This consistent index system ensures that the risk calculation has a clear physical transmission process, so that the zoning results can truly reflect the comprehensive impact of disaster-inducing environment, disaster-causing factors, and disaster-bearing characteristics.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart illustrating the steps of a precise zoning method for ice storm risk in mountainous areas that takes into account slope and aspect corrections; Figure 2 This is a flowchart illustrating the calculation of the basic index of freezing-induced disaster risk in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the calculation of the disaster-bearing body exposure index in an embodiment of the present invention. Detailed Implementation

[0020] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] This invention provides a precise zoning method for ice disaster risk in mountainous areas, considering slope and aspect corrections. This method constructs a joint slope and aspect correction coefficient to correct the basic ice-causing hazard index calculated based on meteorological conditions, grid-by-grid. The corrected hazard index is then combined with the exposure index and vulnerability index of the affected body to obtain a precise zoning result that accurately depicts the impact of mountainous terrain on ice disaster risk. This method features a clear overall process, consistent terminology across all stages, and tight logical relationships, making it widely applicable to decision support for ice prevention and disaster mitigation in fields such as power, transportation, and forestry.

[0022] Please see Figure 1 , Figure 1 This is a flowchart illustrating the implementation of the method of the present invention, which specifically includes the following steps: S100. Obtain basic data for the study area, including daily observation data from meteorological stations, digital elevation model data, data on the distribution of disaster-bearing bodies, and historical records of freezing disasters. S200. Based on the daily observation data of the meteorological stations, calculate the basic index of freezing disaster risk at each spatial grid point in the study area; S300. Using the digital elevation model data, extract the spatial distribution of slope and aspect of the study area, and construct the joint correction coefficient of slope and aspect. S400. The basic index of frost disaster risk is corrected using the slope and aspect joint correction coefficient to obtain the corrected frost disaster risk index. S500. Based on the disaster-bearing body distribution data, calculate the disaster-bearing body exposure index and the disaster-bearing body vulnerability index; S600. Calculate the freezing disaster risk index based on the corrected freezing disaster hazard index, the disaster-bearing body exposure index, and the disaster-bearing body vulnerability index, and classify the risk level based on the freezing disaster risk index to generate a precise zoning map of freezing disaster risk in mountainous areas.

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, a mountainous county is used as an example to illustrate the technical solution of this invention in detail below. The study area covers approximately 3200 square kilometers, with an elevation difference exceeding 1500 meters. The area is characterized by crisscrossing mountains and deep valleys, and is frequently affected by quasi-stationary fronts and cold air infiltration during winter, resulting in frequent freezing rain, rime ice, and other icy weather. It should be noted that the specific numerical values, spatial coordinates, threshold parameters, and scenario examples described in this embodiment are all illustrative and intended only to help understand the technical solution and working principle of this invention. They do not constitute any limitation on the scope of protection of this invention. In actual deployment and application, those skilled in the art can make adaptive adjustments and configurations to the above-mentioned exemplary parameters according to specific factors. Such adjustments and configurations do not depart from the spirit and principles of this invention and should be included within the scope of protection of this invention.

[0024] In step S100, basic data of the study area is acquired, including: daily observation data of meteorological stations in the study area, digital elevation model data, disaster-bearing body distribution data, and historical records of freezing disasters. The daily observation data of meteorological stations specifically includes: daily minimum temperature, daily precipitation, daily average relative humidity, and daily average wind speed, all recorded daily by station, forming a complete meteorological time series data, which serves as the basic input for subsequent daily determination of freezing climate and calculation of annual freezing intensity index.

[0025] In step S200, the basic index of freezing disaster risk for each spatial grid point in the study area is calculated based on daily observation data from meteorological stations. Quantitative indicators characterizing the intensity of freezing disasters are extracted from meteorological observation records and transformed into a unified index usable for risk assessment through spatialization and probability calibration. Figure 2 As shown, the calculation steps for the basic index of disaster risk caused by freezing include: For each meteorological station, freezing weather days are determined using daily minimum temperature, daily precipitation, and daily average relative humidity: a freezing weather day is defined when the daily minimum temperature is ≤0℃, the daily precipitation is ≥1.0 mm, and the daily average relative humidity is ≥80%. For each meteorological station in a given year, only for all identified freezing weather days, calculate the annual freezing intensity index for that station. ,in, For the first Daily precipitation on a freezing weather day The daily average wind speed for the first freezing weather day. and These are the empirical weighting coefficients for precipitation and the wind speed weighting coefficient, respectively. Based on the annual freezing intensity index of various meteorological stations over many years The sequence is spatially interpolated to generate a raster of spatial distribution of multi-year average freezing intensity. Using the historical records of freezing disasters, a mapping relationship is established between different freezing intensity ranges and the probability of disaster. Based on the mapping relationship, the spatial distribution grid of the multi-year average freezing intensity is normalized to obtain a basic index of freezing disaster risk with a value range of 0-1.

[0026] In this embodiment, firstly, for each meteorological station, freezing weather days are determined using daily minimum temperature, daily precipitation, and daily average relative humidity. The criteria for determining a freezing weather day are as follows: when the daily minimum temperature is less than or equal to 0°C, the daily precipitation is greater than or equal to 1.0 mm, and the daily average relative humidity is greater than or equal to 80%, that day is determined as a freezing weather day. A daily minimum temperature less than or equal to 0°C is a necessary condition for supercooled water droplets in the atmosphere to freeze or for the surface temperature of ground objects to drop to the freezing point. Daily precipitation greater than or equal to 1.0 mm ensures sufficient moisture supply for ice accumulation growth, excluding situations where trace amounts of precipitation result in minimal ice thickness that do not pose a real disaster threat. Daily average relative humidity greater than or equal to 80% reflects a near-saturated humid state in the near-surface air, which is conducive to the formation and maintenance of supercooled water droplets. When all three conditions are met simultaneously, the meteorological environment is conducive to the occurrence of freezing weather and the continuous growth of ice accumulation; therefore, that day is designated as a freezing weather day.

[0027] After identifying all freezing weather days, for each meteorological station, within each winter half-year year, the annual freezing intensity index for that station is calculated based solely on all identified freezing weather days within that year. The calculation formula is: ,in, For the first Daily precipitation on a freezing weather day For the first The daily average wind speed on a freezing weather day This is the empirical weighting coefficient for precipitation. This is the empirical weighting coefficient for wind speed. Within a year, the precipitation and wind speed corresponding to all freezing weather days are multiplied by their respective weighting coefficients and then summed to obtain a comprehensive value reflecting the overall level of freezing intensity for that year. Higher precipitation means more moisture is available for ice accumulation on freezing weather days. In this embodiment, the empirical weighting coefficient for precipitation... The value is 0.7, representing the empirical weighting coefficient for wind speed. The value is 0.3. and The method for determining the values ​​is as follows: Collect all historical records of freezing disasters in the study area and surrounding regions that contain entries with explicit descriptions of ice thickness. Use ice thickness as the dependent variable and precipitation and wind speed on the same freezing weather day at the corresponding station as independent variables. Use multiple linear regression to fit the standardized regression coefficients of precipitation and wind speed on ice thickness. Normalize the absolute values ​​of the two standardized regression coefficients to obtain... and The ratio is 7:3, then normalized to... and The sum is 1.0, therefore we take... equals 0.7 It equals 0.3.

[0028] Based on the annual freezing intensity index of various meteorological stations over many years The sequence is spatially interpolated to generate a raster of spatial distribution of multi-year average freezing intensity. In this embodiment, the specific operation is as follows: for every 12 meteorological stations, calculation is performed. The arithmetic mean was used to obtain the multi-year average annual freezing intensity index for each station. Using the longitude and latitude of each station as spatial location variables and its multi-year average annual freezing intensity index as attribute variables, ordinary kriging interpolation was used for spatial interpolation. A spherical model was selected for the semi-variogram model of ordinary kriging interpolation, and the search neighborhood was set to include at least 5 stations and at most all 12 stations. The spatial resolution of the interpolated output raster was set to 100 meters, consistent with the digital elevation model. After interpolation, the raster was masked and clipped using the administrative boundaries of the study area to obtain a spatial distribution raster of multi-year average freezing intensity covering the entire study area. This raster reflects the relative differences in freezing intensity among the grid points in the study area obtained solely from meteorological observation data. Higher-altitude ridge areas and intermontane basins and valleys where cold air easily accumulates have higher freezing intensity values, while lower-altitude river valleys have lower freezing intensity values.

[0029] Using historical records of freezing disasters, a mapping relationship between different freezing intensity ranges and the probability of disaster is established. Based on this mapping relationship, the spatial distribution raster of multi-year average freezing intensity is normalized to obtain a basic index of freezing disaster risk with a value range of 0-1. The specific operation is as follows: The numerical range of the spatial distribution grid of multi-year average freezing intensity is divided into several equally spaced intervals. In this embodiment, 10 intervals are used for division.

[0030] Each incident record in the historical freezing disaster record is located on the corresponding raster cell according to its latitude and longitude coordinates, and the multi-year average freezing intensity index value of that cell is extracted. The number of historical disaster incidents occurring within each freezing intensity interval is counted, and divided by the total number of raster cells contained in that interval to obtain the disaster frequency per unit area for each interval.

[0031] The disaster-causing frequency per unit area corresponding to each interval is normalized according to the maximum value, that is, the frequency of each interval is divided by the maximum frequency of all intervals, so that the normalized value is at most 1 and at least 0.

[0032] Using the median of the multi-year average freezing intensity index for each interval as the x-axis and the normalized disaster-causing frequency per unit area as the y-axis, a curve fitting was performed to establish a mapping function between the freezing intensity index value and the disaster-causing hazard index. This function transformation was applied to each grid point value of the multi-year average freezing intensity spatial distribution grid, mapping the original multi-year average freezing intensity index value to a basic freezing disaster-causing hazard index between 0 and 1. .

[0033] After the above steps, the basic index of the risk of freezing disasters in the study area was obtained. A spatially distributed raster, wherein each grid point of the raster... The value is between 0 and 1. The larger the value, the higher the risk of freezing disasters for that grid point under the background of only considering meteorological conditions. This grid provides the basic input for subsequent correction using the joint correction coefficient of slope and aspect.

[0034] In step S300, the spatial distribution of slope and aspect of the study area is extracted using digital elevation model (DEM) data, and a joint correction coefficient for slope and aspect is constructed. Using DEM data, the slope angle α and aspect angle β of each grid point are calculated at a predetermined target spatial resolution. In this embodiment, the predetermined target spatial resolution is 100 meters. The input data is a 100-meter resolution DEM grid after resampling and projection transformation. The slope angle α of each grid point is calculated and output, representing the maximum tilt angle of the ground surface at that grid point. The aspect angle β of each grid point is also calculated and output. The aspect angle β starts at 0° for due north and gradually increases clockwise, i.e., 90° for due east, 180° for due south, 270° for due west, and 0° for due north (360°). Flat areas do not have a clear orientation, so the slope tool automatically assigns their aspect value to -1. In subsequent processing, grid points with an aspect angle of -1 are treated separately as grid points without aspect and included in the flat slope level for processing.

[0035] After obtaining the raster layers of slope angle α and slope aspect angle β, the slope angle α is divided into four slope levels: flat, gentle, sloping, and steep. The slope aspect angle β is divided into shaded slope group, transition group, and sunny slope group according to the intensity of solar radiation received.

[0036] The criteria for classifying slope grades are as follows: flat slopes correspond to slope angles < 5°, and gentle slopes correspond to slope angles ≤ 5°. <15°, the slope angle corresponding to a slope of 15°≤ <25°, steep slope corresponding to slope angle ≥25°.

[0037] The slope aspect grouping standard is: grouping slope angles... The north slopes (0°-45°, 315°-360°), northeast slopes (45°-90°), and northwest slopes (270°-315°) are classified as shaded slopes, and the slope aspect angles are... The south, southeast, and southwest slopes with angles between 135° and 225° are classified as the sunny slope group, and the remaining slope aspects are... It was designated as a transitional group.

[0038] After completing the slope grade classification and aspect grouping, a correction factor value is pre-set for each combination of slope grade and aspect group, forming a slope-aspect joint correction coefficient assignment table. The slope-aspect joint correction coefficient assignment table is shown in Table 1: Table 1 flat α＜5° 1.15 1.00 0.85 gentle slope 5°≤α＜15° 1.20 1.00 0.80 slope 15°≤α＜25° 1.10 0.95 0.70 steep slope α≥25∘ 0.90 0.80 0.60 In Table 1, the correction factor values ​​for the shady slope group are all greater than 1, indicating an increase in the basic index of freezing disaster risk, reflecting the fact that ice accumulation on shady slopes is difficult to melt, lasts longer, and the actual risk is higher than indicated by meteorological conditions. The correction factor values ​​for the sunny slope group are all less than 1, indicating a decrease in the basic index of freezing disaster risk, reflecting the fact that ice accumulation on sunny slopes melts quickly and the actual risk is lower than indicated by meteorological conditions. The correction factor values ​​for the transition group are between 0 and 1, reflecting that its radiation conditions and risk are at an intermediate level. For the same slope aspect group, as the slope grade changes from flat to gentle slope, sloping slope, and steep slope, the correction factor values ​​generally show a decreasing trend, indicating that the greater the slope, the worse the adhesion stability of the ice. Even under the same radiation conditions, the maximum thickness of ice that can actually adhere stably and continue to grow is smaller, and the risk is correspondingly reduced. Under the flat slope grade, since there is no significant ice-slippage effect caused by slope inclination, the change in correction coefficients is mainly determined by the slope aspect group. The ratio between the shady slope group and the sunny slope group is 1.15 to 0.85, reflecting the moderating effect of pure radiation difference on hazard. Under the steep slope grade, the slope inclination effect is dominant, reflecting the combined influence of radiation difference and slope effect. The specific method for determining the values ​​of each correction coefficient in Table 1 is as follows: by collecting icing observation data of transmission lines at different slopes and aspects in the study area and similar climate zones, using the measured icing thickness of flat open land at the same altitude and under the same meteorological conditions as the benchmark value, comparing the ratio of icing thickness at different slope aspect combinations to the benchmark value, and determining the correction coefficient for each combination according to the statistical average value.

[0039] After constructing the slope and aspect joint correction coefficient assignment table, for each grid point, obtain the correction factor value corresponding to its slope level and the correction factor value corresponding to its aspect group, and then look up the slope and aspect joint correction coefficient corresponding to each grid point in the slope and aspect joint correction coefficient assignment table. We obtained joint correction coefficients for slope and aspect that cover the entire study area and are completely consistent with the raster spatial range of the basic index of freezing disaster risk. Raster layer.

[0040] In step S400, the slope and aspect joint correction coefficient constructed in step S300 is used to correct the basic index of frost-induced disaster risk calculated in step S200, resulting in the corrected frost-induced disaster risk index. The specific operation is as follows: The basic index grid of freezing disaster risk obtained in step S200 is denoted as... The slope and aspect joint correction coefficient grid obtained in step S300 is denoted as... Multiplication is performed point by point, and the calculation formula is: In the formula, The median value of the corrected freezing disaster risk index is obtained by multiplying the basic freezing disaster risk index derived solely from meteorological conditions by the corresponding joint correction coefficient according to the slope grade and aspect group of each grid point. When the grid point is located on a shady, gentle slope... Greater than 1, corrected risk index Greater than the original baseline index of freezing-related disaster risk This reflects that the actual risk at this grid point is higher than the predicted risk value calculated solely based on meteorological conditions. This is especially true when the grid point is located on a sunny, steep slope. Less than 1, corrected risk index Less than the original baseline index of freezing disaster risk This reflects that the actual danger of this grid point is lower than the predicted danger value calculated by meteorological conditions, which makes it difficult for ice accumulation to exist stably for a long time and significantly reduces the actual possibility of disaster.

[0041] After completing the multiplication operation point by point, the calculated The raster is recalibrated to remap its numerical range to 0-1. The recalibration process involves using the "Raster Calculator" in ArcGIS software. Perform linear stretching, where, This is the final output of the corrected ice-induced disaster risk index raster. for Minimum value in the grid for The maximum value in the grid. The corrected freezing-induced disaster risk index after recalibration. The value range has returned to 0-1, consistent with the original basic index of freezing disaster risk. Maintain the same dimensions and comparison benchmarks.

[0042] In step S500, based on the disaster-bearing body distribution data obtained in step S100, the disaster-bearing body exposure index and the disaster-bearing body vulnerability index are calculated, such as... Figure 3 As shown, the calculation process for the exposure index of the disaster-bearing body is as follows: Extract population density distribution data, road network distribution data, and power line distribution data from the disaster-bearing body distribution data, and generate population density raster, road network density raster, and power line density raster respectively. The population density grid, road network density grid, and power line density grid are normalized to 0-1 respectively to obtain normalized population density, normalized road network density, and normalized power line density. A weighted summation method is used to combine the normalized population density, normalized road network density, and normalized power line density, and the combined result is normalized again to 0-1 to obtain the disaster exposure index E. In this embodiment, the weight of power line density is set to 0.45, the weight of road network density is set to 0.35, and the weight of population density is set to 0.20.

[0043] The vulnerability index of the disaster-bearing body is calculated as follows: using the land use and land cover data obtained in step S100, the vulnerability weights are preset according to the degree of loss of different land cover types in historical freezing disasters, and a vulnerability value is directly assigned to each spatial grid point to form a vulnerability index V of the disaster-bearing body with a value range between 0 and 1. In this embodiment, the specific values ​​are: 0.95 for construction land, 0.85 for forest land, 0.60 for orchard land, 0.40 for grassland and shrubland, and 0.10 for water bodies, wetlands, bare land, glaciers and permanent snow cover.

[0044] In step S600, the ice disaster risk index is calculated using the corrected ice disaster hazard index obtained in step S400, the disaster-bearing body exposure index and the disaster-bearing body vulnerability index obtained in step S500. Based on the ice disaster risk index, the risk level is classified, and a precise zoning map of ice disaster risk in mountainous areas is generated.

[0045] First, calculate the freezing disaster risk index. Obtain the corrected risk index of freezing disasters. Exposure index of disaster-bearing bodies and the vulnerability index of disaster-bearing bodies Multiplication is performed point by point, and the calculation formula is: In the formula, This is a risk index for freezing disasters. This is the revised risk index for ice-related disasters. The exposure index of the disaster-bearing body. This is the vulnerability index of the disaster-bearing body. Then, the RI raster is classified into risk levels. This invention employs a grading method based on the data's own distribution characteristics, traversing the effective pixels of the RI raster and finding natural breakpoints in the data as level boundaries. For example, [the method is described in the original text]. The area is designated as a low-risk zone, where the risk of freezing is low, or there are almost no disaster-bearing bodies, or the disaster-bearing bodies are resilient. The area has been designated a medium-risk area, indicating a certain level of disaster risk. Close monitoring and basic preventative measures are necessary. The area has been designated a high-risk zone, meaning it is prone to disasters and suffers heavy losses; therefore, key prevention and control measures and engineering reinforcements should be implemented. The area has been designated as an extremely high-risk zone, indicating that the probability of freezing disasters and the resulting losses are extremely high, requiring the highest level of avoidance and intervention measures.

[0046] After completing the risk level classification, the raster data of the classification results are assigned corresponding colors for rendering, and basic geographic information such as terrain shadows or administrative boundaries are superimposed. The result is output as a complete and accurate zoning map of mountain freezing disaster risk through the geographic information system. This map is used to display the continuous spatial distribution of the four risk levels: low, medium, high, and extremely high, reflecting the refined risk pattern after terrain slope and aspect correction.

[0047] In summary, this invention takes basic data as input, extracts the basic index of freezing disaster risk, constructs and corrects the joint correction coefficient of slope and aspect, and calculates the exposure and vulnerability of the disaster-bearing body. Finally, it generates a freezing disaster risk zoning map that accurately reflects the influence of mountain micro-topography, and realizes a refined spatial characterization of freezing disaster risk in mountainous areas.

[0048] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for precise zoning of mountainous freezing disaster risk considering slope and aspect correction, characterized in that, The specific steps of this method are as follows: S100. Obtain basic data for the study area, including daily observation data from meteorological stations, digital elevation model data, data on the distribution of disaster-bearing bodies, and historical records of freezing disasters. S200. Based on the daily observation data of the meteorological stations, calculate the basic index of freezing disaster risk at each spatial grid point in the study area; S300. Using the digital elevation model data, extract the spatial distribution of slope and aspect of the study area, and construct the joint correction coefficient of slope and aspect. S400. The basic index of frost disaster risk is corrected using the slope and aspect joint correction coefficient to obtain the corrected frost disaster risk index. S500. Based on the disaster-bearing body distribution data, calculate the disaster-bearing body exposure index and the disaster-bearing body vulnerability index; S600. Calculate the freezing disaster risk index based on the corrected freezing disaster hazard index, the disaster-bearing body exposure index, and the disaster-bearing body vulnerability index, and classify the risk level based on the freezing disaster risk index to generate a precise zoning map of freezing disaster risk in mountainous areas.

2. The method for precise zoning of mountain freezing disaster risk considering slope and aspect correction as described in claim 1, characterized in that, In S100, the daily observation data of the meteorological station includes daily minimum temperature, daily precipitation, daily average relative humidity and daily average wind speed; The disaster-bearing body distribution data includes population density distribution data, road network distribution data, power line distribution data, and land use and land cover data.

3. The method for precise zoning of mountain freezing disaster risk considering slope and aspect correction as described in claim 1, characterized in that, In S200, the calculation steps for the basic index of freezing-induced disaster risk include: For each meteorological station, freezing weather days are determined using daily minimum temperature, daily precipitation, and daily average relative humidity: a freezing weather day is defined when the daily minimum temperature is ≤0℃, the daily precipitation is ≥1.0 mm, and the daily average relative humidity is ≥80%. For each meteorological station, the annual freezing intensity index is calculated only for all identified freezing weather days within each year. ,in, For the first Daily precipitation on a freezing weather day For the first The daily average wind speed on a freezing weather day and These are the empirical weighting coefficients for precipitation and the wind speed weighting coefficient, respectively. Based on the annual freezing intensity index of various meteorological stations over many years The sequence is spatially interpolated to generate a raster of spatial distribution of multi-year average freezing intensity. Using the historical records of freezing disasters, a mapping relationship is established between different freezing intensity ranges and the probability of disaster. Based on the mapping relationship, the spatial distribution grid of the multi-year average freezing intensity is normalized to obtain a basic index of freezing disaster risk with a value range of 0-1.

4. The method for precise zoning of mountain freezing disaster risk considering slope and aspect correction as described in claim 1, characterized in that, In S300, the construction of the joint correction coefficient for slope and aspect includes: Using digital elevation model data, the slope angle of each grid point is calculated at a predetermined target spatial resolution. and slope angle ,in With true north as 0°, the angle increases clockwise. Slope angle The slope is divided into four grades: flat, gentle, sloping, and steep, with the slope aspect angle... Based on the intensity of solar radiation received, the slopes are divided into three groups: shady slope, transitional slope, and sunny slope. A correction factor value is pre-set for each combination of slope grade and aspect group, forming a joint correction coefficient assignment table for slope and aspect. For each grid point, the slope correction factor corresponding to the slope grade is obtained. and the slope aspect correction factor corresponding to the slope aspect group. And calculate the joint slope and aspect correction factor for each grid point. ,in, These are values ​​extracted from the slope and aspect joint correction coefficient assignment table based on the grid point slope level. The values ​​are extracted from the slope and aspect joint correction coefficient assignment table based on the grid point aspect group.

5. The method for precise zoning of mountain freezing disaster risk considering slope and aspect correction according to claim 4, characterized in that, The slope grade classification standard is as follows: flatness corresponds to slope angle. <5°, gentle slope corresponds to a slope angle of 5°≤ <15°, the slope angle corresponding to a slope of 15°≤ <25°, steep slope corresponding to slope angle ≥25°; The slope aspect grouping standard is: grouping slope angles... The north slopes (0°-45°, 315°-360°), northeast slopes (45°-90°), and northwest slopes (270°-315°) are classified as shaded slopes, and the slope aspect angles are... The south, southeast, and southwest slopes with angles between 135° and 225° are classified as the sunny slope group, and the remaining slope aspects are... It was designated as a transitional group; In the joint correction coefficient assignment table for slope and aspect, the correction factor value corresponding to the shady slope group is >1, and the correction factor value corresponding to the sunny slope group is <1.

6. The method for precise zoning of mountain freezing disaster risk considering slope and aspect correction as described in claim 1, characterized in that, In S400, the corrected frost-induced disaster risk index is obtained by: acquiring the basic frost-induced disaster risk index and the slope-aspect joint correction coefficient, and then... and the combined slope and aspect correction factor ,according to The corrected freezing disaster risk index is calculated point by point. For the calculated The raster is recalibrated to remap the numerical range to 0-1.

7. The method for precise zoning of mountain freezing disaster risk considering slope and aspect correction as described in claim 1, characterized in that, In S500, the calculation process for the exposure index of the disaster-bearing body is as follows: Extract population density distribution data, road network distribution data, and power line distribution data from the disaster-bearing body distribution data, and generate population density raster, road network density raster, and power line density raster respectively. The population density grid, road network density grid, and power line density grid are normalized to 0-1 respectively to obtain normalized population density, normalized road network density, and normalized power line density. The normalized population density, normalized road network density, and normalized power line density are combined using a weighted summation method, and the combined result is normalized again to 0-1 to obtain the disaster exposure index E.

8. The method for precise zoning of mountain freezing disaster risk considering slope and aspect correction as described in claim 1, characterized in that, In the S500, the vulnerability index of the disaster-bearing body is calculated as follows: using the land use and land cover data in the disaster-bearing body distribution data, based on the degree of loss of different land cover types in historical freezing disasters, a vulnerability weight is preset, and a vulnerability value is directly assigned to each spatial grid point to form a vulnerability index V of the disaster-bearing body with a value range between 0 and 1.

9. A method for precise zoning of mountainous freezing disaster risk considering slope and aspect correction as described in claim 1, characterized in that, In S600, the calculation of the freezing disaster risk index and the classification of risk levels include: Obtain the corrected freezing disaster risk index, disaster-bearing body exposure index, and disaster-bearing body vulnerability index. (The corrected freezing disaster risk index will then be used for further analysis.) Exposure index of disaster-bearing bodies and the vulnerability index of disaster-bearing bodies ,according to Calculate the risk index of freezing disasters Based on the calculated freezing disaster risk index The value range distribution of the freezing disaster risk index The area was designated as a low-risk zone, and the freezing disaster risk index was adjusted. The area was designated as a medium-risk area, and the freezing disaster risk index was increased. The area was designated as a high-risk zone, and the freezing disaster risk index was increased. The area has been designated as an extremely high-risk zone.

Images