A population exposure calculation method and system based on super historical flood events

Abstract

This invention discloses a method and system for calculating population exposure based on super-historical flood events. The method includes: acquiring gridded peak values ​​and daily runoff simulation data from a historical baseline period and comparing them grid-by-grid to obtain a super-historical flood event determination result; classifying each grid cell by socioeconomic level to obtain socioeconomic level classification results; and coupling the super-historical flood event determination result with the socioeconomic level classification result grid-by-grid to obtain the stratified population exposure at each socioeconomic level. This invention can distinguish differentiated exposures across different socioeconomic levels. As a method and system for calculating population exposure based on super-historical flood events, this invention can be widely applied in the field of climate change impact assessment and analysis technology.

Description

Technical Field

[0001] This invention relates to the field of climate change impact assessment and analysis technology, and in particular to a method and system for calculating population exposure based on super-historical flood events. Background Technology

[0002] Against the backdrop of global climate change, unprecedented flood events, exceeding historical observation records, are occurring frequently around the world, posing a significant threat to human socio-economic systems.

[0003] Currently, flood risk identification commonly uses probabilistic statistical thresholds based on return periods, a method that relies on the assumption of stationarity in historical observation data. However, this method has a limitation: super-historical floods are extreme events that exceed the maximum observed value of the historical baseline period and have no historical precedent to refer to. Therefore, traditional methods based on probabilistic extrapolation cannot identify such events.

[0004] Meanwhile, current socioeconomic impact assessment methods typically calculate overall population exposure or use a weighted average of continuous risk indices and population data to calculate a relative score. The limitation of this approach is that it fails to differentiate exposure across different socioeconomic strata and does not achieve separate quantification of exposure at specific socioeconomic strata, thus making it difficult to reveal the social inequalities inherent in climate risk.

[0005] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention aims to provide a method and system for calculating population exposure based on super-historical flood events, capable of distinguishing differentiated exposures across different socioeconomic levels.

[0007] The first technical solution adopted in this invention is: a method for calculating population exposure based on super-historical flood events, comprising the following steps: By acquiring the gridded peak values ​​of historical baseline periods and daily runoff simulation data and comparing them grid by grid, the results of the determination of super-historical flood events are obtained. Each grid unit is classified according to its socioeconomic level to obtain the socioeconomic level classification results; By coupling the results of the determination of super-historical flood events with the results of socio-economic stratification on a grid-by-grid basis, the stratified population exposure of each socio-economic stratum is obtained.

[0008] Furthermore, the step of obtaining the historical baseline period gridded flood peak extreme value and daily runoff simulation data and comparing them grid by grid to obtain the judgment result of the super-historical flood event specifically includes: Historical baseline period gridded flood peak extreme values ​​and daily runoff simulation data are obtained, and the daily runoff simulation data are obtained through simulation output of several global hydrological models; Comparison of historical baseline period gridded flood peak extreme values ​​with daily runoff simulation data; If the daily runoff simulation data of a grid cell is greater than the extreme value of the gridded flood peak in the historical baseline period, it is determined that there is a super-historical flood event in the grid cell, and the grid cell is marked as 1. If the daily runoff simulation data of a grid cell is less than or equal to the gridded peak value of the historical baseline period, it is determined that no super-historical flood event has occurred in the grid cell, and the grid cell is marked as 0. Based on the labeling results of each grid cell, the results of the determination of super-historical flood events are output.

[0009] Furthermore, the step of obtaining the extreme values ​​of the gridded flood peaks in the historical baseline period specifically includes: Based on the historical baseline daily runoff simulation dataset, the annual maximum flood peak flow of the daily runoff sequence of each grid unit is extracted year by year; By comparing the annual maximum flood peak discharge of each grid unit within the historical baseline period, the maximum annual maximum flood peak discharge corresponding to each grid unit is selected as the gridded flood peak extreme value of the historical baseline period.

[0010] Furthermore, the step of classifying each grid unit according to its socioeconomic level to obtain the socioeconomic level classification result specifically includes: Obtain the per capita daily GDP raster data for each grid cell, which is calculated by combining GDP raster data and population raster data; Set a first division threshold, a second division threshold, and a third division threshold, wherein the first division threshold is less than the second division threshold, and the second division threshold is less than the third division threshold; Based on the per capita daily GDP raster data of each grid unit, the data is divided using the first, second, and third division thresholds to obtain the socioeconomic classification results.

[0011] Furthermore, the step of dividing the data based on the per capita daily GDP raster data of each grid unit, combined with the first, second, and third dividing thresholds, to obtain the socioeconomic hierarchy classification results specifically includes: If the per capita daily GDP raster data of a grid cell is less than or equal to the first division threshold, then the grid cell is divided into a region with an extremely low socioeconomic level. If the per capita daily GDP raster data of a grid cell is greater than the first division threshold and less than or equal to the second division threshold, then the grid cell is divided into a low-to-medium socioeconomic level region. If the per capita daily GDP raster data of a grid cell is greater than the second division threshold and less than or equal to the third division threshold, then the grid cell is divided into a medium-to-high socioeconomic level region. If the per capita daily GDP raster data of a grid cell is greater than the third division threshold, then the grid cell is divided into a high socioeconomic level region. By combining regions with extremely low socioeconomic strata, low-to-medium socioeconomic strata, medium-to-high socioeconomic strata, and high socioeconomic strata, the socioeconomic strata classification results are obtained.

[0012] Furthermore, the step of coupling the results of the determination of super-historical flood events with the socio-economic hierarchical classification results grid-by-grid to obtain the stratified population exposure of each socio-economic level specifically includes: Based on the socioeconomic hierarchical classification results, a binary mask corresponding to the current level is generated for each grid, and binary mask data for each level is obtained. The results of the determination of super-historical flood events are coupled with binary mask data of each level grid by grid to screen the corresponding target grid cells. The target grid cells have super-historical flood events and belong to the current socio-economic level classification results to be statistically analyzed. Obtain the population raster data of the target grid cell, and accumulate the exposed population according to the socioeconomic level classification results to obtain the stratified population exposure of each socioeconomic level.

[0013] The second technical solution adopted in this invention is: a population exposure calculation system based on super-historical flood events, comprising: The first module is used to obtain the gridded peak values ​​of historical baseline periods and daily runoff simulation data, and to compare them grid by grid to obtain the judgment results of super-historical flood events. The second module is used to classify each grid unit according to its socio-economic level and obtain the socio-economic level classification results. The third module is used to couple the results of the determination of super-historical flood events with the results of socio-economic hierarchical classification on a grid-by-grid basis to obtain the stratified population exposure of each socio-economic level.

[0014] The beneficial effects of the method and system of this invention are as follows: This invention obtains the gridded peak flood extreme value of the historical baseline period and daily runoff simulation data and compares them grid by grid to obtain the judgment result of super-historical flood events. The gridded peak flood extreme value of the historical baseline period is the physical upper limit of the peak flow experienced by each grid during the observation period, which serves as a non-probabilistic, historical experience-based deterministic threshold benchmark for judging super-historical flood events, and can accurately identify super-historical flood events. Furthermore, each grid unit is classified by socioeconomic level to obtain socioeconomic level classification results. The separate quantification of the exposed population at each socioeconomic level can distinguish the differentiated exposure of different socioeconomic levels. Finally, the super-historical flood event judgment result is coupled with the socioeconomic level classification result grid by grid to obtain the stratified population exposure of each socioeconomic level, and the grid unit that simultaneously meets the two conditions of super-historical flood occurrence and belonging to a specific socioeconomic level can be calculated. Attached Figure Description

[0015] Figure 1 This is a flowchart of the steps of a method for calculating population exposure based on super-historical flood events according to the present invention; Figure 2 This is a structural block diagram of a population exposure calculation system based on super-historical flood events according to the present invention; Figure 3 This is a schematic diagram of the process for identifying super-historical flood events provided in a specific embodiment of the present invention; Figure 4 This is a schematic diagram of the framework for identifying super-historical flood events provided in a specific embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the exposure trends of super-historical floods at different socioeconomic levels, provided by a specific embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0017] Reference Figure 1 This invention provides a method for calculating population exposure based on super-historical flood events, the method comprising the following steps: S100. Obtain the gridded peak values ​​of historical baseline periods and daily runoff simulation data, and compare them grid by grid to obtain the judgment results of super-historical flood events; Specifically, the process involves acquiring historical baseline period gridded flood peak extreme values ​​and daily runoff simulation data, whereby the daily runoff simulation data is obtained through simulation output from several global hydrological models. The historical baseline period gridded flood peak extreme values ​​and daily runoff simulation data are compared. If the daily runoff simulation data of a grid cell is greater than the historical baseline period gridded flood peak extreme value, the grid cell is determined to have experienced a super-historical flood event and is marked as 1. If the daily runoff simulation data of a grid cell is less than or equal to the historical baseline period gridded flood peak extreme value, the grid cell is determined not to have experienced a super-historical flood event and is marked as 0. Based on the marking results of each grid cell, the super-historical flood event determination result is output.

[0018] In this embodiment, the daily runoff data within the simulated period are compared with the gridded peak values ​​of the historical baseline period grid by grid to determine whether a super-historical flood event has occurred in each grid cell.

[0019] For event determination, during grid-by-grid comparison, the daily runoff within the target simulation period is numerically compared with the corresponding grid's historical extreme upper boundary. When the runoff value of a certain grid on a certain day exceeds this boundary, i.e., exceeds the maximum flow since historical observation records began, it is determined that a super-historical flood event has occurred in that grid cell, and a super-historical flood event determination result is generated for that grid, with a value of 1. Conversely, if it does not exceed the threshold benchmark, it is determined that no event has occurred, and the index can be set to 0.

[0020] The daily runoff simulation dataset is derived from the simulation outputs of multiple global hydrological models. Based on the outputs of each hydrological model, corresponding historical baseline gridded flood peak extreme values ​​are constructed, and grid-by-grid determination of super-historical flood events is performed under the corresponding model outputs. After obtaining the statistical results corresponding to each model, the results of each model are then weighted and aggregated to obtain the aggregate statistical results.

[0021] Specifically, based on the historical baseline period daily runoff simulation dataset, the annual maximum flood peak flow of the daily runoff sequence of each grid unit is extracted year by year; the annual maximum flood peak flow of each grid unit in the historical baseline period is compared, and the maximum annual maximum flood peak flow corresponding to each grid unit is selected as the gridded flood peak extreme value of the historical baseline period.

[0022] Furthermore, regarding the benchmark construction, the annual maximum peak flow of the daily runoff sequence for each grid cell is extracted year by year from the quality-controlled historical benchmark period daily runoff simulation dataset. The annual maximum peak flow of each grid cell within the historical benchmark period is compared, and the maximum value of the grid cell in all years within the benchmark period is finally selected. Based on this, a historical peak extreme value upper boundary covering the entire spatial range and at the grid scale is constructed. The historical extreme value upper boundary is the physical upper limit of the peak flow experienced by each grid cell within the observation period, serving as a non-probabilistic, historical experience-based deterministic threshold benchmark for judging super-historical flood events.

[0023] S200. Classify each grid unit according to its socio-economic level to obtain the socio-economic level classification results; Specifically, the per capita daily GDP raster data of each grid unit is obtained; a first division threshold, a second division threshold, and a third division threshold are set, wherein the first division threshold is less than the second division threshold, and the second division threshold is less than the third division threshold; based on the per capita daily GDP raster data of each grid unit, the first division threshold, the second division threshold, and the third division threshold are combined to perform division processing to obtain the socioeconomic level classification result.

[0024] More specifically, if the per capita daily GDP raster data of a grid unit is less than or equal to the first classification threshold, the grid unit is classified as an extremely low socioeconomic level region; if the per capita daily GDP raster data of a grid unit is greater than the first classification threshold and less than or equal to the second classification threshold, the grid unit is classified as a low-to-medium socioeconomic level region; if the per capita daily GDP raster data of a grid unit is greater than the second classification threshold and less than or equal to the third classification threshold, the grid unit is classified as a high-to-medium socioeconomic level region; if the per capita daily GDP raster data of a grid unit is greater than the third classification threshold, the grid unit is classified as a high socioeconomic level region; combining the extremely low socioeconomic level region, the low-to-medium socioeconomic level region, the high socioeconomic level region, and the high socioeconomic level region yields the socioeconomic level classification result.

[0025] S300: The results of the determination of super-historical flood events are coupled with the results of socio-economic hierarchical classification on a grid-by-grid basis to obtain the stratified population exposure of each socio-economic level.

[0026] Specifically, based on the socioeconomic stratification classification results, a binary mask corresponding to the current stratum is generated for each grid, resulting in binary mask data for each stratum. The results of the determination of super-historical flood events are coupled with the binary mask data for each stratum grid by grid to filter the corresponding target grid cells. The target grid cells have super-historical flood events and belong to the current socioeconomic stratification classification results to be counted. The population raster data of the target grid cells are obtained, and the exposed population is accumulated in combination with the socioeconomic stratification classification results to obtain the stratified population exposure of each socioeconomic stratum.

[0027] In this embodiment, based on the global economic line standard and the per capita daily GDP raster data of each grid, an algorithm classifies each grid into four categories: extremely low socioeconomic level, low-to-medium socioeconomic level, medium-to-high socioeconomic level, and high socioeconomic level. A binary mask corresponding to the current socioeconomic level is generated for each grid. The results of the super-historical flood event determination are coupled grid-by-grid with the socioeconomic level binary mask data to calculate grid cells that simultaneously meet the two conditions of experiencing a super-historical flood and belonging to a specific socioeconomic level. Combining the population raster data of each grid cell, the exposed population is accumulated according to the four socioeconomic levels to obtain the stratified population exposure for each socioeconomic level. For grids where the indicator shows no super-historical flood event (e.g., a value of 0), their population is not included in the exposure of any socioeconomic level.

[0028] In summary, the embodiments of the present invention enable the individual quantification of the exposed population at each socioeconomic level, providing a new technical means for flood risk assessment in the context of climate change, and have important scientific and application value.

[0029] Finally, the embodiments of the present invention will be described in conjunction with the accompanying drawings: This embodiment focuses on global land areas to analyze super-historical flood events under different warming scenarios. Specifically, as follows... Figure 3 As shown.

[0030] Daily runoff simulation results from multiple global hydrological models within the ISIMIP3b framework were selected as the base data, with a historical baseline period of 1901–2014. The target simulation period covers future climate simulation periods under various warming scenarios. Gridded peak flood values ​​for the corresponding historical baseline period were constructed based on the outputs of each hydrological model, and grid-by-grid determination of super-historical flood events was performed under the corresponding model outputs. After obtaining the statistical results for each model, the results were then weighted and aggregated to obtain the ensemble statistical results.

[0031] The identification of super-historical flood events includes the following: Benchmark Construction. From the quality-controlled historical baseline daily runoff simulation dataset, the annual maximum peak flow of the daily runoff sequence for each grid cell is extracted year by year. Then, the annual maximum peak flow of each grid cell within the historical baseline period is compared, and the maximum value for that grid cell across all years within the baseline period is selected. Based on this, a gridded peak flow extreme value for the historical baseline period, covering the entire spatial range and at the grid scale, is constructed as the super-historical threshold benchmark. This gridded peak flow extreme value is the physical upper bound of the peak flow experienced by each grid cell during the observation period, serving as a non-probabilistic, purely historical, deterministic threshold benchmark for judging super-historical flood events.

[0032] Event identification. During grid-by-grid comparison, the daily runoff within the target simulation period is numerically compared with the gridded peak value of the corresponding grid. When the runoff value of a grid on a certain day exceeds the boundary, i.e., exceeds the maximum flow since historical observation records began for that grid, the super-historical flood event is determined to have occurred, and a super-historical flood event determination result is generated for that grid with a value of 1. Conversely, if the threshold benchmark is not exceeded, it is determined that no super-historical flood event has occurred, and the index value is 0.

[0033] Furthermore, this embodiment calculates the population exposure at different socioeconomic levels based on the obtained results of the determination of super-historical flood events.

[0034] Based on the global economic baseline standard (extremely low socioeconomic level: $1.90 / day; low-to-middle socioeconomic level: $3.20 / day; high-to-middle socioeconomic level: $5.50 / day), a classification operation is performed on each grid cell based on the per capita daily GDP raster data. Specifically, the per capita GDP value of each grid cell is compared sequentially with the three thresholds mentioned above, thereby classifying the grid cell into one of four categories: extremely low socioeconomic level, low-to-middle socioeconomic level, high-to-middle socioeconomic level, or high socioeconomic level. A binary mask corresponding to the current socioeconomic level is generated for each grid cell. This binary mask indicates whether each grid cell belongs to a specific socioeconomic level; it is set to 1 if it belongs and 0 if it does not.

[0035] Subsequently, the output results of the super-historical flood event determination (with values ​​of 0 or 1) are coupled grid-by-grid with the aforementioned hierarchical binary mask data (with values ​​of 0 or 1). Specifically, a grid is marked as an effective exposure unit at a specific socioeconomic level only if it simultaneously meets both the conditions of a super-historical flood event occurring and belonging to a specific socioeconomic level. For grids with a super-historical flood event determination result of 0, regardless of their socioeconomic level classification, the population of that grid is not included in the exposure of any level.

[0036] The population raster data for each grid cell is acquired, and the total population of that grid cell is calculated by combining the geographical area of ​​the grid cell. Based on the results of grid-by-grid coupling, the exposed population of the effectively exposed units is accumulated according to the four socioeconomic strata to obtain the stratified population exposure of the four strata.

[0037] like Figure 5 The figure shows the exposure trends to super-historical floods at different socioeconomic levels obtained using the method of this embodiment. The exposure levels of all levels increase with rising temperatures, but the growth rate of exposure at the socioeconomic level is significantly higher than that at the non-socioeconomic level, verifying the ability of this method to quantify the social inequalities of super-historical flood risk.

[0038] Reference Figure 2 A population exposure calculation system based on super-historical flood events, comprising: The first module 201 is used to obtain the gridded peak values ​​of historical baseline periods and daily runoff simulation data and compare them grid by grid to obtain the judgment results of super-historical flood events. The second module 202 is used to classify each grid unit according to its socio-economic level and obtain the socio-economic level classification results. The third module 203 is used to couple the results of the determination of super-historical flood events with the results of socio-economic hierarchical classification on a grid-by-grid basis to obtain the stratified population exposure of each socio-economic level.

[0039] This embodiment includes a super-historical flood identification unit and a social inequality analysis unit, which are used to compare grid by grid to determine whether a super-historical flood event has occurred in each grid unit; and to classify each grid unit into socio-economic levels and calculate the stratified population exposure.

[0040] Specifically, such as Figure 4 As shown, this includes a super-historical flood identification unit and a social inequality analysis unit. The super-historical flood identification unit is used to perform the benchmark construction and grid-by-grid comparison operations described in Example 1 to determine whether a super-historical flood event has occurred in each grid cell; the social inequality analysis unit is used to perform the socioeconomic hierarchy classification, grid-by-grid coupling, and stratified exposure accumulation calculations described in Example 2.

[0041] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0042] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this is not intended to limit the scope of the embodiments of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for calculating population exposure based on super-historical flood events, characterized in that, Includes the following steps: By acquiring the gridded peak values ​​of historical baseline periods and daily runoff simulation data and comparing them grid by grid, the results of the judgment of super-historical flood events are obtained. Each grid unit is classified according to its socioeconomic level to obtain the socioeconomic level classification results; By coupling the results of the determination of super-historical flood events with the results of socio-economic stratification on a grid-by-grid basis, the stratified population exposure of each socio-economic stratum is obtained.

2. The method for calculating population exposure based on super-historical flood events according to claim 1, characterized in that, The step of obtaining the extreme values ​​of gridded flood peaks from the historical baseline period and comparing them with daily runoff simulation data, and then comparing them grid by grid to obtain the determination result of the super-historical flood event, specifically includes: Historical baseline period gridded flood peak extreme values ​​and daily runoff simulation data are obtained, and the daily runoff simulation data are obtained through simulation output of several global hydrological models; Comparison of historical baseline period gridded flood peak extreme values ​​with daily runoff simulation data; If the daily runoff simulation data of a grid cell is greater than the extreme value of the gridded flood peak in the historical baseline period, it is determined that there is a super-historical flood event in the grid cell, and the grid cell is marked as 1. If the daily runoff simulation data of a grid cell is less than or equal to the gridded peak value of the historical baseline period, it is determined that no super-historical flood event has occurred in the grid cell, and the grid cell is marked as 0. Based on the labeling results of each grid cell, the results of the determination of super-historical flood events are output.

3. The method for calculating population exposure based on super-historical flood events according to claim 2, characterized in that, The step of obtaining the extreme values ​​of the gridded flood peak in the historical baseline period specifically includes: Based on the historical baseline daily runoff simulation dataset, the annual maximum peak flow of the daily runoff sequence of each grid unit is extracted year by year; By comparing the annual maximum flood peak discharge of each grid unit within the historical baseline period, the maximum annual maximum flood peak discharge corresponding to each grid unit is selected as the gridded flood peak extreme value of the historical baseline period.

4. The method for calculating population exposure based on super-historical flood events according to claim 3, characterized in that, The step of classifying each grid unit according to its socioeconomic level to obtain the socioeconomic level classification result specifically includes: Obtain the per capita daily GDP raster data for each grid cell, which is calculated by combining GDP raster data and population raster data; Set a first division threshold, a second division threshold, and a third division threshold, wherein the first division threshold is less than the second division threshold, and the second division threshold is less than the third division threshold; Based on the per capita daily GDP raster data of each grid unit, the data is divided using the first, second, and third division thresholds to obtain the socioeconomic classification results.

5. The method for calculating population exposure based on super-historical flood events according to claim 4, characterized in that, The step of classifying socioeconomic levels based on the per capita daily GDP raster data of each grid unit, combined with the first, second, and third classification thresholds, specifically includes: If the per capita daily GDP raster data of a grid cell is less than or equal to the first division threshold, then the grid cell is divided into a region with an extremely low socioeconomic level. If the per capita daily GDP raster data of a grid cell is greater than the first division threshold and less than or equal to the second division threshold, then the grid cell is divided into a low-to-medium socioeconomic level region. If the per capita daily GDP raster data of a grid cell is greater than the second division threshold and less than or equal to the third division threshold, then the grid cell is divided into a medium-to-high socioeconomic level region. If the per capita daily GDP raster data of a grid cell is greater than the third division threshold, then the grid cell is divided into a high socioeconomic level region. By combining regions with extremely low socioeconomic strata, low-to-medium socioeconomic strata, medium-to-high socioeconomic strata, and high socioeconomic strata, the socioeconomic strata classification results are obtained.

6. The method for calculating population exposure based on super-historical flood events according to claim 5, characterized in that, The step of coupling the results of the determination of super-historical flood events with the socio-economic hierarchical classification results grid by grid to obtain the stratified population exposure of each socio-economic level specifically includes: Based on the socioeconomic hierarchical classification results, a binary mask corresponding to the current level is generated for each grid, and binary mask data for each level is obtained. The results of the determination of super-historical flood events are coupled with binary mask data of each level grid by grid to screen the corresponding target grid cells. The target grid cells have super-historical flood events and belong to the current socio-economic level classification results to be statistically analyzed. Obtain the population raster data of the target grid cell, and accumulate the exposed population according to the socioeconomic level classification results to obtain the stratified population exposure of each socioeconomic level.

7. A population exposure calculation system based on super-historical flood events, characterized in that, Includes the following modules: The first module is used to obtain the gridded peak flood extreme value and daily runoff simulation data of the historical baseline period and compare them grid by grid to obtain the judgment result of the super-historical flood event. The second module is used to classify each grid unit according to its socio-economic level and obtain the socio-economic level classification results. The third module is used to couple the results of the determination of super-historical flood events with the results of socio-economic hierarchical classification on a grid-by-grid basis to obtain the stratified population exposure of each socio-economic level.

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