Typhoon disaster risk assessment method for quantifying wind and rain combined loss

Abstract

The invention discloses a typhoon disaster risk assessment method for quantifying wind and rain combined loss, and belongs to the field of typhoon disaster risk early warning and loss assessment, and the method comprises the steps: determining the grid range of a typhoon core disaster area based on typhoon center path data and wind circle size data; constructing a typhoon wind and rain comprehensive index by using the wind speed and rainfall rasterized data sequence; the counted county-level typhoon disaster direct economic loss is scientifically distributed to a grid scale; taking the calculated typhoon wind and rain comprehensive index as an independent variable, taking rasterized typhoon disaster direct economic loss statistical data as a dependent variable, adopting a four-parameter Logistic function for fitting, and constructing a typhoon disaster vulnerability curve model. According to the method, the typhoon vulnerability curve model considering the wind and rain disaster-induced capability is constructed, the mathematical relationship between the economic loss and the disaster intensity is established, the limitation of a traditional qualitative evaluation method is overcome, and a scientific and accurate quantitative basis is provided for the economic influence of typhoon disasters.

Description

Technical Field

[0001] This invention belongs to the field of typhoon disaster risk early warning and loss assessment technology, specifically involving a method for quantifying the combined wind and rain losses of typhoon disasters. Background Technology

[0002] Typhoons are large-scale low-pressure weather systems that form over the ocean, often accompanied by severe storms and heavy rains. Furthermore, due to global climate change, typhoon activity characteristics have altered, leading to a significant increase in economic losses and social impacts caused by typhoon disasters. Current research on the hazard and impact of typhoon disasters is primarily qualitative, using multiple variables to weighted calculations and threshold methods to define hazard levels. These methods lack quantitative models of typhoon impacts, resulting in a lack of theoretical basis for predicting economic losses from typhoon disasters. There is an urgent need for refined assessments of economic losses caused by typhoon disasters to provide technical support for disaster reduction in industry and agriculture, urban disaster response, disaster loss assessment, and reconstruction. Currently, existing technologies for assessing typhoon disaster risk mainly rely on indicator systems and statistical inference methods based on vulnerability curves.

[0003] However, these methods still face the following problems: 1) The indicator system method is difficult to quantify typhoon risk and can only be used to evaluate possible risk levels. Furthermore, the weighting of indicators in this method relies too heavily on expert experience, resulting in significant uncertainty. In addition, while the indicator system method increases model complexity by considering more influencing factors, the coupling mechanism between these factors is unclear, reducing the model's reliability to some extent. 2) The vulnerability curve method is only suitable for disaster risk assessment on a large scale. For small-scale areas, the scarcity of disaster samples leads to insufficient model generalization ability. Furthermore, this method struggles to consider the impact of other disaster factors (such as the disaster-prone environment and disaster prevention and mitigation capabilities). 3) The main disaster-causing factors of typhoons are strong winds and heavy rainfall. Currently, only a few models comprehensively consider the effects of wind and rain, and none of them account for the significantly different disaster-causing capabilities of wind and rain. Therefore, there is an urgent need to develop a new method that can more accurately assess the disaster-causing capabilities of typhoon winds and rain. Summary of the Invention

[0004] To address the shortcomings of existing technologies, such as the lack of a reasonable and effective comprehensive typhoon wind and rain index and quantitative model for typhoon disaster risk assessment, and the lack of theoretical basis for predicting economic losses from typhoon disasters, this invention proposes a typhoon disaster risk assessment method that quantifies the combined wind and rain losses. This method, based on refined gridded economic loss data and the typhoon's impact range and path, constructs a new comprehensive typhoon wind and rain index to quantitatively assess the disaster-causing capacity of typhoons' strong winds and heavy rainfall under different surface conditions, and builds a reasonable typhoon disaster vulnerability curve. This provides a scientific basis for typhoon disaster early warning, disaster prevention, post-disaster economic loss assessment, and reconstruction.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A method for quantifying the combined wind and rain losses in typhoon disaster risk assessment includes the following steps:

[0007] S1. Obtain the optimal path data of the typhoon center and the size data of the typhoon's level 8 wind circle for the target typhoon event. Use the size range of the typhoon's level 8 wind circle as the core disaster-causing area of ​​the typhoon and calculate the rasterized range of the core disaster-causing area of ​​the target typhoon event.

[0008] S2. Obtain wind speed and rainfall observation data of the core disaster-causing area during the period from the generation to the end of the target typhoon event, and rasterize them. Based on the rasterized wind speed and rainfall data, construct the typhoon gale intensity index and the typhoon rainfall intensity index. Combine the constructed typhoon gale intensity index and the typhoon rainfall intensity index to construct the typhoon wind and rain comprehensive index.

[0009] S3. Based on the typhoon wind and rain comprehensive index constructed by S2, obtain the statistical data of direct economic loss of typhoon disaster in the target county of the core disaster area and the county division data, and rasterize the statistical data of direct economic loss of typhoon disaster in the target county to obtain rasterized data of direct economic loss of typhoon disaster in the target county.

[0010] S4. Using the typhoon wind and rain composite index calculated in S2 as the independent variable and the rasterized data of direct economic losses from typhoon disasters in the target county obtained in S3 as the dependent variable, the four-parameter Logistic function is used to fit the relationship between the typhoon wind and rain composite index and the rasterized data of direct economic losses from typhoon disasters in the target county, and a typhoon disaster vulnerability curve model is constructed to conduct typhoon disaster risk assessment.

[0011] Furthermore, the specific steps for calculating the core disaster-causing rasterized range of the target typhoon event in S1 are as follows:

[0012] S1.1 Typhoon Center Point Rasterization: Based on the acquired optimal typhoon path data, the latitude and longitude coordinates of the typhoon center recorded at a preset time resolution are mapped to the corresponding spatial raster points to construct a typhoon center point sequence.

[0013] S1.2 Rasterization of the Core Disaster-Causing Area of ​​the Target Typhoon Event: Based on the calculated typhoon center grid sequence, the radius of the typhoon's level 8 wind circle in four quadrants is extracted with each typhoon center grid as the origin. The straight-line distance between the remaining grid points and the corresponding typhoon center grid points is calculated. The specific calculation formula is as follows: ,

[0014] in, For grid points Distance from the grid points of the typhoon center For grid points The longitude of the center For grid points The latitude of the center The longitude of the grid center of the typhoon center. The latitude of the grid center of the typhoon center;

[0015] Determine whether the distance between the grid point and the corresponding typhoon center grid point is less than the quadrant radius corresponding to the typhoon's level 8 wind circle; if it is less, it means that the grid point is located in the typhoon's core disaster area; if it is greater, it means that the grid point is not located in the typhoon's core disaster area.

[0016] S1.3 Calculate the typhoon core disaster area corresponding to each typhoon center grid point, merge all typhoon core disaster areas, and thus obtain the rasterized range of the core disaster area of ​​the target typhoon event.

[0017] Furthermore, the process of constructing the typhoon wind intensity index and typhoon rainfall intensity index in S2 is as follows:

[0018] Based on the wind speed and rainfall observation data of the core disaster-causing area during the period from the generation to the end of the target typhoon event, the data is rasterized according to the preset time and grid resolution to generate spatial rasterized data sequences of wind speed and rainfall.

[0019] Calculate the cumulative wind speed at each grid point during the period from the formation to the end of the target typhoon event, and determine the maximum cumulative wind speed value among all grid points in the typhoon's core disaster area. Based on the maximum value normalization method, calculate the typhoon gale intensity index for each grid point. The specific calculation process is as follows:

[0020] ,

[0021] ,

[0022] ,

[0023] In the formula, For grid points The typhoon gale intensity index, It is a grid. The cumulative wind speed during the period from the formation to the end of the target typhoon event. It is the largest cumulative wind speed value among all grid points. It is a grid. In the Wind speed at a recorded moment, It is the total number of records with a preset time resolution interval. These represent the number of longitude and latitude grid points calculated according to the preset grid resolution, respectively.

[0024] Calculate the cumulative rainfall at each grid point during the period from the formation to the end of the target typhoon event, and determine the maximum cumulative rainfall among all grid points in the typhoon's core disaster area. Based on the maximum value normalization method, calculate the typhoon rainfall intensity index for each grid point. The specific calculation process is as follows:

[0025] ,

[0026] ,

[0027] ,

[0028] In the formula, It is a grid. Typhoon rainfall intensity index, It is a grid. The cumulative rainfall during the period from the formation to the end of the target typhoon event. It is the largest cumulative rainfall among all grid points. It is a grid. In the Rainfall at each recorded moment, It is the total number of records with a preset time resolution interval.

[0029] Furthermore, combining the constructed typhoon gale intensity index and typhoon rainfall intensity index, a comprehensive typhoon wind and rain index is constructed, calculated using the following formula:

[0030] ,

[0031] ,

[0032] ,

[0033] In the formula, and These represent the weights of the ability of strong winds and heavy rainfall to affect the ground, respectively. Representative grid points The proportion of urban area is determined by statistically analyzing the range of urban area proportion values ​​for 90% of the grid points. Value; when The range of values ​​for is determined as follows Grid Classified as a town grid point Value , Value , These represent the disaster-causing capacity of strong winds and rainfall in urban areas, respectively; when The range of values ​​for is determined as follows Grid It was identified as a rural grid point. Value , Value , These represent the disaster-causing capacity of strong winds and rainfall in rural areas, respectively.

[0034] Furthermore, in order to determine the optimal... The parameters are combined, and a systematic grid search method is used for parameter optimization. The specific steps are as follows:

[0035] Step 1: Calculate the area ratio of urban areas point by point. The calculation formula is as follows:

[0036] ,

[0037] In the formula, For grid points The proportion of urban area, For grid points The area of ​​the urban area, For grid points The land area;

[0038] Step 2, parameter space discretization, including: The range of values The urban area is discretized uniformly with a step size of 0.1, and the area proportion is divided into... Groups and Groups; grouped according to the proportion of urban area. All A discrete value between these values ​​constitutes several sets to be searched. Parameter pair combination;

[0039] Step 3, Multi-dimensional Calculation: For each The parameters are combined to calculate the typhoon wind and rain composite index, rasterize the statistical data of direct economic losses from typhoon disasters in the target county, and fit the relationship between the typhoon wind and rain composite index and the rasterized data of direct economic losses from typhoon disasters in the target county based on the four-parameter Logistic function.

[0040] Step 4: Select the optimal fitting result based on the four-parameter Logistic function. Parameter pair combination.

[0041] Furthermore, the specific process of rasterizing the statistical data on direct economic losses from typhoon disasters in target counties within S3 includes:

[0042] S3.1 Calculate the economic loss distribution index, specifically as follows:

[0043] The effective area proportion supporting economic activities at a preset grid resolution is calculated using high-resolution land use and cover change (LUCC) data, and used as the weight for land cover classification. The specific calculation formula is as follows:

[0044] ,

[0045] In the formula, For grid points The percentage of land area where economic activity takes place. Grid points at preset grid resolution area, Grid points at preset grid resolution Internally, the area of ​​land with economic activity calculated based on high-resolution land use / cover change data;

[0046] Based on the digital elevation model, the proportion of land area within a grid point whose slope meets the threshold requirement is calculated at a preset grid resolution and used as the slope weight. The specific calculation formula is as follows:

[0047] ,

[0048] In the formula, For grid points The percentage of land area whose internal slope meets the threshold requirement. Grid points at preset grid resolution area, Grid points at preset grid resolution The land area whose internal slope meets the threshold requirement;

[0049] The economic loss allocation index is obtained by multiplying the land cover classification weight, slope weight, and typhoon wind and rain composite index. The calculation formula is as follows:

[0050] ,

[0051] In the formula, For grid points The economic loss distribution index, Grid The corresponding typhoon wind and rain index;

[0052] S3.2 Calculate the economic loss allocation weight for grid points: The target county contains N grid points that meet the preset grid resolution. Each grid point contains an economic loss allocation index. Then, the grid points within the target county... The formula for weighting the allocation of economic losses is:

[0053] ,

[0054] In the formula, Target county grid The weighting of economic losses The sum of the economic loss allocation indices for N grid points in the target county;

[0055] S3.3 Calculate the gridded economic loss values ​​to complete the rasterization of the statistical data on direct economic losses from typhoon disasters in the target county. The calculation formula is as follows:

[0056] ,

[0057] In the formula, Target county grid The economic loss value, This refers to the statistical value of direct economic losses caused by typhoon disasters in the target county.

[0058] Furthermore, the formula used in S4 to fit the relationship between the typhoon storm surge index and the rasterized data of direct economic losses from typhoon disasters in the target county using the four-parameter Logistic function is as follows:

[0059] ,

[0060] In the formula, The grid point economic loss value, The grid point represents the typhoon's combined wind and rain index. The minimum grid point economic loss value, The maximum grid point economic loss value. for The median or logarithmic midpoint, The initial slope of the logarithmically transformed data is fitted using linear regression;

[0061] In each Under parameter pair combinations, the relationship between the typhoon wind and rain composite index and the rasterized data of direct economic losses from typhoon disasters in the target county is fitted based on the four-parameter Logistic function;

[0062] The coefficient of determination (R²) and root mean square error (RMSE) were used as evaluation indicators to assess the goodness of fit of the typhoon disaster vulnerability curve model, and the model that achieves the optimal fitting accuracy was selected. The parameter pair combination is used as the final solution to obtain the typhoon disaster vulnerability curve model.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0064] 1) This invention constructs a new typhoon wind and rain comprehensive index by introducing the concept of wind and rain disaster-causing capacity, which overcomes the defect of the traditional wind and rain comprehensive index which defaults to the ability to cause complete disaster, and improves the usability and scope of application of the typhoon wind and rain comprehensive index in assessing typhoon disaster risk;

[0065] 2) This invention combines remote sensing inversion and satellite observation data of typhoon paths and level 8 wind circles to achieve fine rasterization of the typhoon's impact range at the event level, thereby improving the accuracy of typhoon disaster range determination.

[0066] 3) This invention constructs a vulnerability curve model with typhoon wind and rainfall intensity, land use, slope and urban area ratio as input variables and disaster loss as output variable. This model can take into account the comprehensive impact of multiple disaster-causing factors and surface conditions of typhoons, and realize the prediction of economic losses of a single typhoon event at the grid scale and county scale.

[0067] 4) The model constructed in this invention can provide decision support for governments, enterprises and insurance institutions, optimize the allocation of disaster prevention and mitigation resources, and reduce the economic losses and social impacts caused by typhoon disasters; based on quantitative loss prediction, it can provide a scientific basis for disaster insurance pricing, post-disaster compensation and emergency plan formulation, and improve the society's ability to resist typhoon disasters. Attached Figure Description

[0068] Figure 1 This is a flowchart of the method of the present invention;

[0069] Figure 2 A rasterized graph showing the relationship between the Typhoon Wind and Rain Composite Index (TWRCI) and the direct economic losses caused by Typhoon Mangkhut under the optimal parameter combination.

[0070] Figure 3 A comparison chart of rasterized data on direct economic losses in counties of Hainan Island and data on direct economic losses calculated by the model under the optimal parameter combination;

[0071] Figure 4 The scatter distribution and curve shape of the typhoon vulnerability curve model of Hainan Island obtained by fitting a four-parameter Logistic function under the optimal parameter combination. Detailed Implementation

[0072] The technical solution of the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0073] like Figure 1 As shown, the present invention provides a method for quantifying the combined wind and rain loss of typhoon disasters, comprising the following steps:

[0074] S1. Obtain the optimal path data of the typhoon center and the size data of the typhoon's 8-level wind circle from the Tropical Cyclone Data Center of the China Meteorological Administration. Use the size of the 8-level wind circle as the core disaster-causing area of ​​the typhoon, and calculate the rasterized range of the core disaster-causing area of ​​the "Mangkut" typhoon event; specifically including:

[0075] S1.1 Based on the obtained typhoon optimal path dataset, the latitude and longitude coordinates recorded every 3 hours are mapped to the corresponding spatial raster grid points to obtain the typhoon center grid point sequence.

[0076] S1.2 Based on the computational typhoon center grid sequence, the four-quadrant radius of the typhoon's level 8 gale circle with each typhoon center grid as the origin is extracted. The straight-line distance between the remaining grid points and the corresponding typhoon center grid points is calculated. The specific calculation formula is as follows: ,

[0077] in, For grid points Distance from the grid points of the typhoon center For grid points The longitude of the center For grid points The latitude of the center The longitude of the grid center of the typhoon center. The latitude of the grid center of the typhoon center;

[0078] Determine whether the distance between the grid point and the grid point at the center of the typhoon is less than the radius of the quadrant corresponding to the typhoon's level 8 wind circle; if it is less, it means that the grid point is located in the typhoon's core disaster area; if it is greater, it means that the grid point is not located in the typhoon's core disaster area.

[0079] S1.3 Calculate the typhoon core disaster area corresponding to each typhoon center grid point, merge all typhoon core disaster areas, and thus obtain the rasterized range of the core disaster area of ​​the "Mangkut" typhoon event.

[0080] S2. Obtain wind speed and precipitation observation data for the core disaster-causing area during the period from the formation to the end of Typhoon "Mangkut," and rasterize them. Based on the rasterized wind speed and precipitation data, construct the typhoon gale intensity index and the typhoon rainfall intensity index; combine the typhoon gale intensity index and the typhoon rainfall intensity index to construct the typhoon wind and rain comprehensive index; specifically including:

[0081] S2.1 Obtain half-hour rainfall data from the time of the target typhoon event from its generation to its end on the official website of the Global Satellite Precipitation Program. Then, aggregate the half-hour data with a grid resolution of 0.1°×0.1° into a three-hour cumulative rainfall data sequence using a spatiotemporal resampling method, thus obtaining a cumulative rainfall data sequence with a time resolution of 3 hours and a grid resolution of 0.1°.

[0082] Hourly 10-meter wind speed component data of ERA5-Land were acquired from the European Centre for Medium-Range Weather Forecasts (ECMWF) platform during the period from the generation to the end of the target typhoon event, and hourly wind speed modulus data were calculated. Based on the wind speed modulus sequence, the grid resolution was reduced from 0.25° to 0.1° using bilinear interpolation, and the hourly wind speed modulus data were subjected to temporal convolution processing with a 3-hour moving average window. Finally, the maximum wind speed data sequence was output, which was spatiotemporally aligned with the rainfall data.

[0083] Calculate the cumulative wind speed at each grid point during the period from the formation to the end of the target typhoon event, and determine the maximum cumulative wind speed value among all grid points in the core disaster area. Based on the maximum value normalization method, calculate the typhoon gale intensity index for each grid point. The specific calculation process is as follows:

[0084] ,

[0085] ,

[0086] ,

[0087] In the formula, For grid points The typhoon gale intensity index, It is a grid. The cumulative wind speed during the period from the formation to the end of the target typhoon event. It is the largest cumulative wind speed value among all grid points. It is a grid. In the Wind speed at a recorded moment, This refers to the total number of records at three-hour intervals. These represent the number of grid points for longitude and latitude calculated at a grid resolution of 0.1°, respectively.

[0088] The cumulative rainfall at each grid point during the period from the formation to the end of Typhoon "Mangkut" was calculated, and the maximum cumulative rainfall among all grid points in the core disaster area was determined. Based on the maximum value normalization method, the typhoon rainfall intensity index of each grid point was calculated. The specific calculation process is as follows:

[0089] ,

[0090] ,

[0091] ,

[0092] In the formula, It is a grid. Typhoon rainfall intensity index It is a grid. The cumulative rainfall during the period from the formation to the end of the target typhoon event. It is the largest cumulative rainfall among all grid points. It is a grid. In the Rainfall at each recorded moment, This is the total number of records at three-hour intervals.

[0093] S2.2 Combining the constructed typhoon wind intensity index and typhoon rainfall intensity index, and integrating the disaster-causing effects of strong winds and rainfall, a comprehensive typhoon wind and rain index (TWRCI) is constructed, calculated using the following formula:

[0094] ,

[0095] ,

[0096] ,

[0097] In the formula, and These represent the weights of the ability of strong winds and rainfall to affect the ground, respectively. Representative grid points The proportion of urban area is determined by statistically analyzing the proportion of urban area in 90% of the grid points, which ranges from [0, 0.1]. Therefore, the value of T is set to 0.1. The range of values ​​for is determined as follows Grid Classified as a town grid point Value , Value , These represent the disaster-causing capacity of strong winds and rainfall in a city, respectively; when The range of values ​​for is determined as follows Grid It was identified as a rural grid point. Value , Value , These represent the disaster-causing capacity of strong winds and rainfall in rural areas, respectively; The range of values The grid is uniformly discretized with a step size of 0.1, and each grid point is determined based on the proportion of the town's area. , , and The values ​​are all one of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0. Formed by combining in pairs A group refers to 10,000 sets of parameters;

[0098] S3. Based on the typhoon wind and rain comprehensive index calculated in S2, obtain statistical data on direct economic losses from typhoons in target counties of Hainan Island, as well as county-level administrative division data. Then, rasterize the statistical data on direct economic losses from typhoons in target counties of Hainan Island to obtain rasterized data on direct economic losses from typhoons in target counties of Hainan Island. Specifically, this includes:

[0099] S3.1 The economic loss distribution index is as follows:

[0100] Download high-resolution land use / cover change data, extract grid points with economic activity, including built-up areas and farmland, and calculate the effective area proportion of economically active grid points at a grid resolution of 0.1° as the land cover classification weight. The calculation formula is as follows:

[0101] ,

[0102] In the formula, For grid points The percentage of land area where economic activity takes place. For grid points area, For grid points Internally, the area of ​​land with economic activity calculated based on high-resolution land use / cover change data;

[0103] Slope data is calculated using digital elevation model (DEM) data. The percentage of land area with a slope less than 25° within a grid point at a grid resolution of 0.1° is used as the slope weight. The specific calculation formula is as follows:

[0104] ,

[0105] In the formula, For grid points The percentage of land area with an inward slope of less than 25° For grid points area, For grid points Land area with an inward slope of less than 25°;

[0106] The economic loss allocation index is obtained by multiplying the land cover classification weight, slope weight, and typhoon wind and rain composite index. The calculation formula is as follows:

[0107] ,

[0108] In the formula, For grid points The economic loss distribution index, Grid The corresponding typhoon wind and rain index;

[0109] S3.2 Calculate the economic loss allocation weight for grid points: Assuming the target county contains N grid points with a grid resolution of 0.1°, and each grid point contains an economic loss allocation index, then the grid points within the target county... The formula for weighting the allocation of economic losses is:

[0110] ,

[0111] In the formula, Target county grid The weighting of economic losses The sum of the economic loss allocation indices for N grid points in the target county

[0112] S3.3 Calculate the gridded economic loss values ​​to complete the rasterization of the statistical data on direct economic losses from typhoon disasters in the target county. The calculation formula is as follows:

[0113] ,

[0114] In the formula, Target county grid The economic loss value, The statistical value of direct economic losses caused by typhoon disasters in the target county;

[0115] S4. Using the typhoon wind and rain composite index calculated in S2 as the independent variable and the rasterized data of typhoon disaster economic losses in the target county obtained in S3 as the dependent variable, the relationship between the typhoon wind and rain composite index and the rasterized data of direct economic losses from typhoon disasters in the target county is fitted based on the four-parameter Logistic function to construct a typhoon disaster vulnerability curve model and conduct typhoon disaster risk assessment.

[0116] The fitting formula for the four-parameter Logistic function is:

[0117] ,

[0118] In the formula, The grid point economic loss value, The grid point represents the typhoon's combined wind and rain index. The minimum grid point economic loss value, The maximum grid point economic loss value. for The median or logarithmic midpoint, The initial slope of the logarithmically transformed data is fitted using linear regression;

[0119] Through experiments with 10,000 sets of parameters (10*10)² set by S2, the urban area was obtained. and At that time, rural areas and The optimal model fit was achieved when the weights of the typhoon wind intensity index and the typhoon rainfall intensity index were 1.0 for urban areas and 0.2 for rural areas, and 0.7 and 0.3 for rural areas. Figure 2 The relationship between the Typhoon Wind and Rain Composite Index (TWRCI) and rasterized data of direct economic losses from Typhoon Mangkhut is shown under the optimal parameter combination. Figure 3 The paper presents a comparison between rasterized data of direct economic losses in counties of Hainan Island and economic losses calculated by the model under the optimal parameter combination. Figure 4 The scatter distribution and curve shape of the typhoon vulnerability curve model for Hainan Island, fitted under the optimal parameter combination, are shown. The coefficient of determination (R²) is 0.6883, indicating that the model has a good fitting effect.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made by those skilled in the art within the scope of the technology disclosed in this invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of this invention. Therefore, the protection scope of this invention should be determined by the scope of the claims.

Claims

1. A method for quantifying the combined wind and rain losses in typhoon disaster risk assessment, characterized in that, Includes the following steps: S1. Obtain the optimal path data of the typhoon center and the size data of the typhoon's level 8 wind circle for the target typhoon event. Use the size range of the typhoon's level 8 wind circle as the core disaster-causing area of ​​the typhoon and calculate the rasterized range of the core disaster-causing area of ​​the target typhoon event. S2. Obtain wind speed and rainfall observation data of the core disaster-causing area during the period from the generation to the end of the target typhoon event, and rasterize them. Based on the rasterized wind speed and rainfall data, construct the typhoon gale intensity index and the typhoon rainfall intensity index. Combine the constructed typhoon gale intensity index and the typhoon rainfall intensity index to construct the typhoon wind and rain comprehensive index. S3. Based on the typhoon wind and rain comprehensive index constructed by S2, obtain the statistical data of direct economic loss of typhoon disaster in the target county of the core disaster area and the county division data, and rasterize the statistical data of direct economic loss of typhoon disaster in the target county to obtain rasterized data of direct economic loss of typhoon disaster in the target county. S4. Using the typhoon wind and rain composite index calculated in S2 as the independent variable and the rasterized data of direct economic losses from typhoon disasters in the target county obtained in S3 as the dependent variable, the four-parameter Logistic function is used to fit the relationship between the typhoon wind and rain composite index and the rasterized data of direct economic losses from typhoon disasters in the target county, and a typhoon disaster vulnerability curve model is constructed to conduct typhoon disaster risk assessment.

2. The typhoon disaster risk assessment method for quantifying combined wind and rain losses according to claim 1, characterized in that, The specific steps for calculating the core disaster-causing rasterized range of the target typhoon event in S1 are as follows: S1.1 Typhoon Center Point Rasterization: Based on the acquired optimal typhoon path data, the latitude and longitude coordinates of the typhoon center recorded at a preset time resolution are mapped to the corresponding spatial raster points to construct a typhoon center point sequence. S1.2 Rasterization of the Core Disaster-Causing Area of ​​the Target Typhoon Event: Based on the calculated typhoon center grid sequence, the radius of the typhoon's level 8 wind circle in four quadrants is extracted with each typhoon center grid as the origin. The straight-line distance between the remaining grid points and the corresponding typhoon center grid points is calculated. The specific calculation formula is as follows: , in, For grid points Distance from the grid points of the typhoon center For grid points The longitude of the center For grid points The latitude of the center The longitude of the grid center of the typhoon center. The latitude of the grid center of the typhoon center; Determine whether the distance between the grid point and the corresponding typhoon center grid point is less than the quadrant radius corresponding to the typhoon's level 8 wind circle; if it is less, it means that the grid point is located in the typhoon's core disaster area; if it is greater, it means that the grid point is not located in the typhoon's core disaster area. S1.3 Calculate the typhoon core disaster area corresponding to each typhoon center grid point, merge all typhoon core disaster areas, and thus obtain the rasterized range of the core disaster area of ​​the target typhoon event.

3. The typhoon disaster risk assessment method for quantifying combined wind and rain losses according to claim 1, characterized in that, The process of constructing the typhoon wind intensity index and typhoon rainfall intensity index in S2 is as follows: Based on the wind speed and rainfall observation data of the core disaster-causing area during the period from the generation to the end of the target typhoon event, the data is rasterized according to the preset time and grid resolution to generate spatial rasterized data sequences of wind speed and rainfall. Calculate the cumulative wind speed at each grid point during the period from the formation to the end of the target typhoon event, and determine the maximum cumulative wind speed value among all grid points in the core disaster area. Based on the maximum value normalization method, calculate the typhoon gale intensity index for each grid point. The specific calculation process is as follows: , , , In the formula, For grid points The typhoon gale intensity index, It is a grid. The cumulative wind speed during the period from the formation to the end of the target typhoon event. It is the largest cumulative wind speed value among all grid points. It is a grid. In the Wind speed at a recorded moment, It is the total number of records with a preset time resolution interval. These represent the number of longitude and latitude grid points calculated according to the preset grid resolution, respectively. Calculate the cumulative rainfall at each grid point during the period from the formation to the end of the target typhoon event, and determine the maximum cumulative rainfall among all grid points in the core disaster area. Based on the maximum value normalization method, calculate the typhoon rainfall intensity index for each grid point. The specific calculation process is as follows: , , , In the formula, It is a grid. Typhoon rainfall intensity index, It is a grid. The cumulative rainfall during the period from the formation to the end of the target typhoon event. It is the largest cumulative rainfall among all grid points. It is a grid. In the Rainfall at each recorded moment, It is the total number of records with a preset time resolution interval.

4. The typhoon disaster risk assessment method for quantifying combined wind and rain losses according to claim 3, characterized in that, Combining the constructed typhoon gale intensity index and typhoon rainfall intensity index, a comprehensive typhoon wind and rain index is constructed, calculated using the following formula: , , ， In the formula, and These represent the weights of the ability of strong winds and heavy rainfall to affect the ground, respectively. Representative grid points The proportion of urban area is determined by statistically analyzing the range of urban area proportion values ​​for 90% of the grid points. Value; when The range of values ​​for is determined as follows Grid Classified as a town grid point Value , Value , These represent the disaster-causing capacity of strong winds and rainfall in urban areas, respectively; when The range of values ​​for is determined as follows Grid It was identified as a rural grid point. Value , Value , These represent the disaster-causing capacity of strong winds and rainfall in rural areas, respectively.

5. A typhoon disaster risk assessment method for quantifying combined wind and rain losses as described in claim 4, characterized in that, To determine the optimal The parameters are combined, and a systematic grid search method is used for parameter optimization. The specific steps are as follows: Step 1: Calculate the area ratio of urban areas point by point. The calculation formula is as follows: ， In the formula, For grid points The proportion of urban area, For grid points The area of ​​the urban area, For grid points The land area; Step 2, parameter space discretization, including: The range of values The urban area is discretized uniformly with a step size of 0.1, and the area proportion is divided into... Groups and Groups; grouped according to the proportion of urban area. All A discrete value between these values ​​constitutes several sets to be searched. Parameter pair combination; Step 3, Multi-dimensional Calculation: For each The parameters are combined to calculate the typhoon wind and rain composite index, rasterize the statistical data of direct economic losses from typhoon disasters in the target county, and fit the relationship between the typhoon wind and rain composite index and the rasterized data of direct economic losses from typhoon disasters in the target county based on the four-parameter Logistic function. Step 4: Select the optimal fitting result based on the four-parameter Logistic function. Parameter pair combination.

6. The typhoon disaster risk assessment method for quantifying combined wind and rain losses according to claim 1, characterized in that, The specific process of rasterizing the statistical data on direct economic losses from typhoon disasters in target counties in S3 includes: S3.1 Calculate the economic loss distribution index, specifically as follows: High-resolution land use / cover change data is used to calculate the effective area proportion for economic activities at a preset grid resolution, which is then used as the weight for land cover classification. The specific calculation formula is as follows: ， In the formula, For grid points The percentage of land area where economic activity takes place. Grid points at preset grid resolution area, Grid points at preset grid resolution Internally, the area of ​​land with economic activity calculated based on high-resolution land use / cover change data; Based on the digital elevation model, the proportion of land area within a grid point whose slope meets the threshold requirement is calculated at a preset grid resolution and used as the slope weight. The specific calculation formula is as follows: ， In the formula, For grid points The percentage of land area whose internal slope meets the threshold requirement. Grid points at preset grid resolution area, Grid points at preset grid resolution The land area whose internal slope meets the threshold requirement; The economic loss allocation index is obtained by multiplying the land cover classification weight, slope weight, and typhoon wind and rain composite index. The calculation formula is as follows: ， In the formula, For grid points The economic loss distribution index, Grid The corresponding typhoon wind and rain index; S3.2 Calculate the economic loss allocation weight for grid points: The target county contains N grid points that meet the preset grid resolution. Each grid point contains an economic loss allocation index. Then, the grid points within the target county... The weighting of economic losses is as follows: , In the formula, Target county grid The weighting of economic losses The sum of the economic loss allocation indices for N grid points in the target county that meet the preset grid resolution; S3.3 Calculate the gridded economic loss values ​​to complete the rasterization of the statistical data on direct economic losses from typhoon disasters in the target county. The calculation formula is as follows: , In the formula, Target county grid The economic loss value, This refers to the statistical value of direct economic losses caused by typhoon disasters in the target county.

7. The typhoon disaster risk assessment method for quantifying combined wind and rain losses according to claim 6, characterized in that, The formula used in S4 to fit the relationship between the typhoon storm storm index and the rasterized data of direct economic losses from typhoon disasters in the target county using a four-parameter Logistic function is as follows: ， In the formula, The grid point economic loss value, The grid point represents the typhoon's combined wind and rain index. The minimum grid point economic loss value, The maximum grid point economic loss value. for The median or logarithmic midpoint, The initial slope of the logarithmically transformed data is fitted using linear regression; In each Under parameter pair combinations, the relationship between the typhoon wind and rain composite index and the rasterized data of direct economic losses from typhoon disasters in the target county is fitted using a four-parameter Logistic function; the optimal fitting accuracy is achieved. The parameter pair combination is used as the final solution to obtain the typhoon disaster vulnerability curve model.

8. The typhoon disaster risk assessment method for quantifying combined wind and rain losses according to claim 7, characterized in that, The coefficient of determination and root mean square error were used as evaluation indicators to assess the goodness of fit of the typhoon disaster vulnerability curve model, and the model that achieves the optimal fitting accuracy was selected. Parameter pair combination.

Images