Method for estimating socioeconomic exposure to combined rainstorm and wind disasters under climate change

Abstract

This application discloses a method for estimating the socioeconomic exposure to combined disasters of heavy rain and high winds under climate change. In this technical solution, the impact of extreme precipitation and high winds on the socioeconomic system is considered at the same time, and the inconsistency characteristics of the hydrological series under the influence of climate change are fully considered. The maximum possible weight function is used to analyze future two-dimensional risk changes, which has a strong physical meaning and statistical basis, and can effectively characterize the changing characteristics of future extreme climate disasters under climate change. Moreover, this application combines global climate models, two-variable analysis, and socioeconomic effect assessment methods, which can provide an important and highly operational reference basis for basin disaster assessment and early warning under a changing environment, and provide engineering reference value for responding to future climate disasters and scientifically formulating emission reduction strategies.

Description

Technical Field

[0001] The present application relates to the technical field of hydrological disaster assessment, and in particular to a method for estimating the socioeconomic exposure to combined disasters of heavy rain and high winds under climate change. Background Art

[0002] Since the Second Industrial Revolution, human society has used a large amount of fossil fuels, greenhouse gas emissions in the atmosphere have increased sharply, the energy balance and material cycle processes of the earth's land and atmosphere have changed, the uneven temporal and spatial distribution of water resources has intensified, and extreme hydrological and meteorological disasters such as rainstorms and floods have occurred frequently, posing major challenges to the sustainable development of human society and ecosystems. Precipitation is an important factor and key variable in the hydrological cycle. Since the 21st century, the intensity and frequency of extreme precipitation in most parts of the world have increased significantly, triggering a series of water disasters such as mountain torrents, urban waterlogging, and mudslides, resulting in global losses of more than US$30 billion each year. For example, in 2020, the Yangtze River experienced several large-scale heavy rainfall processes, forming a basin-wide flood, causing direct economic losses of nearly 70 billion yuan; in July 2021, Zhengzhou, Henan Province was hit by heavy rainstorms, causing 14.814 million people to be affected, 16.203 million mu of crops were affected, and direct economic losses were 133.715 billion yuan. In order to meet the above challenges, it is urgent to strengthen the study of the evolution of extreme precipitation under climate change. According to the Clausius-Clapeyron thermodynamic equation, the atmospheric saturated water vapor pressure increases at a rate of about 6.8% / ℃ with climate warming, and in recent years, scholars have predicted that the intensity and frequency of global extreme precipitation will continue to increase by the end of this century. Since extreme precipitation processes are often accompanied by strong atmospheric energy and water transport processes, they may cause high wind events. High winds are also one of the natural disasters that cause great damage to the socio-economic system and human society, and have had a significant impact on many regions around the world in recent years. However, existing literature rarely explores the changing characteristics of heavy rain and high wind compound disasters under climate change, and has failed to estimate the socio-economic exposure of heavy rain and high wind compound disasters under future scenarios. Summary of the invention

[0003] In view of this, the present application provides a method for estimating the socioeconomic exposure to combined disasters of heavy rain and high winds under climate change, which can effectively and accurately estimate the socioeconomic exposure to combined disasters of heavy rain and high winds under climate change.

[0004] The present application provides a method for estimating the socioeconomic exposure to combined disasters of heavy rain and high winds under climate change, the method comprising:

[0005] Obtain daily precipitation data of multi-satellite fusion precipitation products for the target area to be estimated and wind speed data of the atmospheric reanalysis data set for the target area, obtain daily precipitation data and wind speed data output by the global climate model under the shared socio-economic path, and obtain population and GDP data under the shared socio-economic path;

[0006] Correcting the daily precipitation data and wind speed data output by the global climate model based on the daily precipitation data of the multi-satellite fusion precipitation product and the wind speed data of the atmospheric reanalysis data set of the target area;

[0007] Based on the daily precipitation data and wind speed data output by the calibrated global climate model, the threshold values ​​of extreme precipitation and extreme wind speed of each global climate model in the historical period are deduced;

[0008] Based on the threshold, the historical and future rainstorm and wind composite events of the global climate model are defined, and the wind intensity and precipitation accumulation intensity in the rainstorm and wind composite events are extracted;

[0009] Constructing a time-varying most likely weight function for the gale intensity and precipitation accumulation intensity, and obtaining the multi-mode socioeconomic exposure caused by the increase of future combined disasters of heavy rain and gale based on the time-varying most likely weight function;

[0010] According to the socioeconomic exposure under the multiple models, the socioeconomic exposure under future climate change scenarios is obtained.

[0011] Optionally, the “correcting daily precipitation data and wind speed data output by the global climate model” includes:

[0012] The trend items of the observation data and historical simulation series of meteorological variables are derived, that is, the observation data of satellite or reanalysis products in each month from 1985 to 2014 are recorded as The simulated data of GCMs for each month in the historical period are recorded as The simulated data of GCMs for each month in a future period of a scenario are recorded as Separately deduce and The interannual trend line is then translated to make the sum of the trend items of each year equal to 0; for the series whose interannual trend is not significant at the 0.05 confidence level, the trend items of each year are reset to 0; then the trend items of each series are removed to obtain three new series. and

[0013] Calculate the historical period under different quantiles and scenario period deviation, and based on the true observed value Obtain pseudo-observation series for the scenario period Where: p is the different quantiles set, and 100 equally spaced quantiles are taken in the range of 0.01 to 0.99;

[0014] in accordance with Scenario simulation series The frequency distribution curve is bias-corrected, and the corrected future series is obtained based on quantile mapping. Adding the trend term deducted in the first step, the corrected future scenario is finally obtained.

[0015] Optionally, the threshold is obtained in the following manner:

[0016] The daily precipitation after correction in each global climate model historical period is ranked, and the daily precipitation intensity exceeding the 99th percentile is selected as the threshold of extreme precipitation;

[0017] The corrected daily horizontal wind speeds for each GCM historical period were ranked, and the daily wind speeds exceeding the 99% quantile were selected as the threshold for extreme winds.

[0018] Optionally, the “define the historical and future rainstorm and wind composite events of the global climate model based on the threshold value” includes:

[0019] Based on the precipitation thresholds of the historical periods of the global climate models, the precipitation days exceeding the precipitation thresholds in the historical period are selected as extreme precipitation events, and then the events with a continuous occurrence time exceeding a first preset number of days are selected from the extreme precipitation events as rainstorm events;

[0020] Based on the high wind thresholds of each global climate model historical period, events exceeding the high wind thresholds in the historical period are selected as extreme high wind events;

[0021] When the extreme high wind event occurs during the rainstorm event within a preset time period, it is confirmed that a combined rainstorm and high wind disaster has occurred.

[0022] Optionally, the constructing of the time-varying most likely weight function is constructed by using a Copula function.

[0023] Optionally, the method of “obtaining socioeconomic exposure under future climate change scenarios” is arithmetic averaging.

[0024] Compared with the prior art, the estimation method of this application has the following beneficial effects:

[0025] 1. Scientific and reasonable, close to engineering practice;

[0026] This application takes into account the impact of extreme precipitation and high winds on the socio-economic system, and fully considers the inconsistency characteristics of the hydrological series under the influence of climate change. It uses the maximum possible weight function to analyze future two-dimensional risk changes. It has a strong physical meaning and statistical basis, and can effectively characterize the changing characteristics of future extreme climate disasters under climate change.

[0027] 2. It can provide engineering reference value for responding to future climate disasters and formulating emission reduction strategies scientifically. This application combines global climate models, two-variable analysis and socio-economic effect assessment methods, which can provide important and highly operational reference basis for basin disaster assessment and early warning under a changing environment, and provide engineering reference value for responding to future climate disasters and formulating emission reduction strategies scientifically. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0029] Figure 1 A specific flow chart of the method is provided for the embodiment of the present application;

[0030] Figure 2 Several typical schematic diagrams of combined rainstorm and high wind events are provided for the embodiments of the present application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0032] In the description of the present application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0033] The method for estimating the socioeconomic exposure to the combined disaster of heavy rain and high winds under climate change in this application includes the following steps:

[0034] Step 1: Collect daily precipitation data of multi-satellite fusion precipitation products and wind speed data of reanalysis datasets in the target area to be evaluated (i.e., the study area), collect global climate model outputs under three shared socioeconomic pathways, and obtain population and GDP data of shared socioeconomic pathway datasets; specifically, the following steps are included:

[0035] 1.1 Based on the second-generation global multi-satellite fusion quantitative precipitation product (MSWEP-V2) developed by Princeton University in the United States, the daily precipitation series in the study area from 1985 to 2014 was obtained;

[0036] The MSWEP-V2 dataset is the second generation of global multi-satellite fusion quantitative precipitation products released by Princeton University in the United States. It has a spatiotemporal resolution of 0.1° and 3h, integrates multiple satellite inversion data sources, more than 70,000 ground stations and reanalysis precipitation data, and corrects the land precipitation magnitude based on global measured runoff data. This example uses the daily precipitation data of the MSWEP-V2 dataset from 1985 to 2014.

[0037] 1.2 Based on the ERA5 dataset of the European Centre for Medium-Range Weather Forecasts, wind speed data in two directions are obtained and the daily horizontal wind speed series from 1985 to 2014 is derived.

[0038] ERA5 is the fifth generation of atmospheric reanalysis data set of the European Centre for Medium-Range Weather Forecasts, with a spatial resolution of 0.25°, providing hourly meteorological data covering the globe since 1979. This embodiment obtains hourly wind speed data in the ERA5 data set for the study area during 1985-2014. The wind speed data of the ERA5 data set includes hourly data in the north-south direction and the east-west direction. This embodiment first uses the Pythagorean theorem to deduce the hourly horizontal wind speed through the hourly data in the two directions, and then takes the average wind speed of 24 hours on each day as the horizontal wind speed on a daily scale.

[0039] 1.3 Collect daily precipitation and wind speed data from global climate model outputs under the three shared socioeconomic pathways.

[0040] In order to estimate the future climate scenario, the five global climate models (GCMs) newly released by the International Coupled Model Inter-comparison Project Phase 6 (CMIP6) are used. CMIP6 uses the matrix framework of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The five GCMs selected in this application are: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0 and UKESM1-0-LL. The selected scenarios include three scenarios for the historical period and the future period (SSP126, SSP370 and SSP585). The meteorological variables selected are daily precipitation and daily horizontal wind speed data. The historical period is set to 1985-2014, and the future period is set to 2071-2100.

[0041] 1.4 Obtain population and GDP data from the Shared Socio-Economic Pathways dataset;

[0042] In order to assess the socioeconomic risks that drought events may cause, the population and GDP data of three shared socioeconomic pathways, namely sustainable development (SSP1), moderate development (SSP2) and conventional development (SSP5), are considered and combined with the corresponding greenhouse gas emission scenarios RCP, and the output data of three matrix frameworks, SSP126, SSP370 and SSP585, are used. There are many international institutions that provide population and GDP simulation data of shared socioeconomic pathways. This application uses the estimated data set under the "universal two-child policy" open source released by Nanjing University of Information Science and Technology in China. This product considers the results of my country's previous population and economic censuses and annual statistical yearbooks, and estimates China's socioeconomic index from 2010 to 2100 based on the Cobb-Douglas model and the population-development-environment analysis model. This dataset is one of the datasets that is closest to my country's national conditions today and is widely used in the socioeconomic risk assessment of extreme hydrological events.

[0043] Step 2: Using satellite and reanalysis products as a benchmark, bias correction is performed on precipitation and wind speed output from global climate models to obtain daily precipitation and wind speed series under climate change;

[0044] 2.1 Derivation of the trend terms of the observed data of meteorological variables and the simulated series of historical periods respectively.

[0045] The observation data of satellite or reanalysis products for each month from 1985 to 2014 are recorded as The simulated data of GCMs for each month in the historical period are recorded as The simulated data of GCMs for each month in a future period of a scenario are recorded as Separately deduce and The interannual trend line is then shifted to make the sum of the trend items of each year equal to 0; for the series whose interannual trend is not significant at the 0.05 confidence level, the trend items of each year are reset to 0; then, the trend items of each series are removed to obtain three new series. and

[0046] 2.2 Calculate historical periods at different quantiles and scenario period The deviation of the pseudo observation series of the scenario period is obtained based on the real observation values.

[0047]

[0048] Where p is the different quantiles set, and 100 equally spaced quantiles in the range of 0.01 to 0.99 are taken.

[0049] 2.3 Basis Scenario simulation series The frequency distribution curve is bias-corrected, and the corrected future series is obtained based on quantile mapping. The trend term deducted in the first step is added to obtain the corrected future scenario.

[0050] This application considers that the frequency distribution functions of each month may be different to a certain extent, so the data of each GCM in 12 months are calibrated separately, thereby obtaining the meteorological series of 5 GCMs under three scenarios (SSP 126, SSP370, SSP585) in the historical period and the future period.

[0051] Step 3: Based on the corrected data, the thresholds of extreme precipitation and extreme wind speed of each GCM in the historical period are deduced;

[0052] 3.1 The corrected daily precipitation of each GCM historical period was ranked, and the daily precipitation intensity exceeding the 99% quantile was selected as the definition threshold of extreme precipitation events;

[0053] 3.2 The corrected daily horizontal wind speeds for each GCM historical period were ranked, and the daily wind speeds exceeding the 99% quantile were selected as the definition threshold for extreme wind events.

[0054] Step 4: Identify the combined rainstorm and high wind events in each GCM historical period and future period, and deduce the high wind intensity and cumulative precipitation intensity in the event.

[0055] Step 4.1 Based on the precipitation threshold of each GCM historical period obtained in step 3.1, select the precipitation days exceeding the threshold in the historical period as extreme precipitation events, and then select the events that occur continuously for more than 2 days from the extreme precipitation events, which are defined as heavy rain events;

[0056] That is, only when extreme precipitation events occur continuously and last for at least three days can they be called rainstorm events.

[0057] Step 4.2: Based on the high wind thresholds of each GCM historical period obtained in step 3.2, select the events exceeding the thresholds in the historical period as extreme high wind events;

[0058] Step 4.3: For each GCM, traverse each rainstorm event obtained in step 4.1; if one or more extreme gale events in step 4.2 occur during the rainstorm event, within 3 days before the event, or within 3 days after the event, then it is considered that a combined rainstorm and gale event has occurred.

[0059] like Figure 2 As shown, schematic diagrams of several typical heavy rain and high wind combined events are given.

[0060] Step 4.4 optimizes and adjusts the rainstorm and high wind events selected in step 4.3, that is, if multiple extreme high wind events occur in the same rainstorm and high wind event, only the high wind with the largest intensity is selected to represent the high wind intensity, and the total precipitation is used to represent the cumulative precipitation intensity.

[0061] Step 4.5 For each GCM in each scenario, the precipitation threshold and high wind threshold of the historical period are used, and the above steps are used to obtain the heavy rain and high wind composite events in the future period.

[0062] Step 5. Construct the time-varying most likely weight function of the combined disaster of heavy rain and high winds for each grid based on the Copula function, and evaluate the socioeconomic exposure caused by the increase in the risk of combined disasters in the future.

[0063] Step 5.1 Based on the return period, construct the time-varying most likely weight function of the strong wind intensity and precipitation accumulation intensity of each grid under the non-uniform condition;

[0064] This application constructs the joint probability distribution of gale intensity and cumulative precipitation intensity through the Copula function. Different from univariate frequency analysis, the joint recurrence period of two variables is composed of contour lines, which contain countless combinations. How to determine a reasonable combination scenario is extremely critical for risk quantification. Previous studies on complex disaster events mostly selected joint design values ​​based on the same frequency assumption, lacking a statistical basis and making it difficult to capture the objective occurrence laws of drought events. This application constructs the maximum possible weight function based on the recurrence period, and selects the combination scenario of heavy rain and gale composite events based on the maximum principle of the joint probability density function:

[0065]

[0066] Where: c[F HW ,F DR ] is the probability density function of Copula; f HW 、f DR The probability density function that characterizes the intensity of strong winds and the cumulative intensity of precipitation, F HW and F DR is the cumulative distribution function; (d*, s*) represents a joint return period T AND The most likely combination scenario of high wind intensity HW and precipitation cumulative intensity DR; E is the average interval between heavy rain and high wind composite events.

[0067] Step 5.2 derives the socioeconomic exposure caused by the increased risk of heavy rain and high wind combined events in each grid under future climate change;

[0068] In order to quantitatively characterize the potential socioeconomic exposure caused by the increased risk of future heavy rain and high wind combined events, the Newton iteration method is first used to solve equation (2) to obtain the high wind intensity and precipitation cumulative intensity (Dh, Sh) corresponding to a certain return period (Th) in the historical period (1985-2014). Then, a 30-year sliding window (the same as the length of the historical period) is used to construct the time-varying marginal distribution and Copula function of the future period (2015-2100). Dh and Sh are substituted into the time-varying distribution function of the kth sliding window in the future period in turn to calculate the new return period. If T f (k) <T h , it indicates that the two-dimensional drought risk of the kth window increases, and vice versa. For the kth time window, the parameters of the marginal distribution and joint distribution are derived using the data of 15 years before and after the center point, and the socioeconomic exposure in the future period is measured by the following formula:

[0069]

[0070] Where: E pop and E GDP The population and GDP exposure to the increased risk of compound disasters are represented by POP k and GDP k are the population and GDP of the kth year respectively; I(·) is the indicator function, T h -T f When (k)>0, it is recorded as 1, otherwise it is recorded as 0; N1 and N2 represent the starting and ending years of the study period respectively.

[0071] Previous studies usually directly construct the joint distribution of historical and future periods, and define the socioeconomic exposure of the future period as 0 or 100% by comparing the changes in the joint return period of the two periods. This method cannot capture the interannual variation characteristics of the risk of two-dimensional extreme events and the dynamic properties of the socioeconomic index, and there is a certain irrationality. This application can not only consider the inconsistency that may be caused by future climate change, but also explore the interannual characteristics of risk changes and make full use of the dynamic data of shared socioeconomic paths.

[0072] Step 6 uses the arithmetic mean method to obtain the socioeconomic exposure under future climate change scenarios.

[0073] Step 6.1 This application uses five GCMs and three scenarios, so a total of 15 groups of socioeconomic exposures can be obtained. The 15 results are averaged using the arithmetic mean method to obtain the socioeconomic exposure of each grid in the study area.

[0074] 6.2 The ANOVA variance decomposition method is used to quantify the relative uncertainty of each link in the socioeconomic exposure estimate.

[0075] ANOVA (analysis of variance) is a conventional technique in the field of climate impact assessment. It is often used to quantify the uncertainty in various aspects of hydrological simulation under climate change scenarios, but it has not yet been applied to quantify the uncertainty in the estimated socioeconomic exposure caused by future combined disasters of heavy rain and high winds. Since ANOVA is a conventional technique in this field, this application does not go into details about its specific implementation.

[0076] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for estimating socioeconomic exposure to combined disasters of heavy rain and high winds under climate change, characterized in that: The method comprises: Obtain daily precipitation data of multi-satellite fusion precipitation products for the target area to be estimated and wind speed data of the atmospheric reanalysis data set for the target area, obtain daily precipitation data and wind speed data output by the global climate model under the shared socio-economic path, and obtain population and GDP data under the shared socio-economic path; Correcting the daily precipitation data and wind speed data output by the global climate model based on the daily precipitation data of the multi-satellite fusion precipitation product and the wind speed data of the atmospheric reanalysis data set of the target area; Based on the daily precipitation data and wind speed data output by the calibrated global climate model, the threshold values ​​of extreme precipitation and extreme wind speed of each global climate model in the historical period are deduced; Correction of daily precipitation data and wind speed data output by the global climate model includes: The trend items of the observed data and historical simulation series of meteorological variables are derived, and the observed data of satellite or reanalysis products for each month from 1985 to 2014 are recorded as The simulated data of GCMs for each month in the historical period are recorded as The simulated data of GCMs for each month in a future period of a scenario are recorded as Separately deduce and The interannual trend line is then shifted to make the sum of the trend items of each year equal to 0; for the series whose interannual trend is not significant at the 0.05 confidence level, the trend items of each year are reset to 0; then, the trend items of each series are removed to obtain three new series. and Calculate the historical period under different quantiles and scenario period deviation, and based on the true observed value Obtain pseudo-observation series for the scenario period Where: p is the different quantiles set, and 100 equally spaced quantiles are taken in the range of 0.01 to 0.99; in accordance with Scenario simulation series The frequency distribution curve is bias-corrected, and the corrected future series is obtained based on quantile mapping. The trend term deducted in the first step is added to obtain the corrected future scenario. Based on the threshold, the historical and future rainstorm and wind composite events of the global climate model are defined, and the wind intensity and precipitation accumulation intensity in the rainstorm and wind composite events are extracted; Constructing a time-varying most likely weight function for the gale intensity and precipitation accumulation intensity, and obtaining the multi-mode socioeconomic exposure caused by the increase of future combined disasters of heavy rain and gale based on the time-varying most likely weight function; Constructing the time-varying most likely weight function for the gale intensity and the cumulative precipitation intensity includes: constructing the joint probability distribution of the gale intensity and the cumulative precipitation intensity through the Copula function, constructing the maximum possible weight function based on the recurrence period, and selecting the combined scenario of the heavy rain and gale compound event based on the maximum principle of the joint probability density function: Where: c[F HW ,F DR ] is the probability density function of Copula; f HW 、f DR The probability density function that characterizes the intensity of strong winds and the cumulative intensity of precipitation, F HW and F DR is the cumulative distribution function; (d*, s*) represents a joint return period T AND The most likely combination scenario of the intensity of heavy winds HW and the cumulative precipitation intensity DR; E is the average interval between heavy rain and heavy wind composite events; Based on the time-varying most likely weight function, the multi-mode socioeconomic exposure caused by the increase in the future combined disaster of heavy rain and high winds is obtained, including: in order to quantitatively characterize the potential socioeconomic exposure caused by the increase in the risk of combined events of heavy rain and high winds in the future, the Newton iteration method is first used to solve the above formula to obtain the high wind intensity Dh and the cumulative precipitation intensity Sh corresponding to a certain return period Th in the historical period, and then the time-varying marginal distribution and Copula function of the future period are constructed with the time consistent with the length of the historical period as the sliding window; Dh and Sh are substituted into the time-varying distribution function of the kth sliding window in the future period in turn, and the new return period T is calculated. f (k); if T f (k) <T h , it indicates that the two-dimensional drought risk of the k-th sliding window increases, otherwise it decreases. For the k-th sliding window, the parameters of the marginal distribution and joint distribution are derived using the data of 15 years before and after the center point, and the socioeconomic exposure in the future period is measured by the following formula: Where: E pop and E GDP The population and GDP exposure to the increased risk of compound disasters are represented by POP k and GDP k are the population and GDP of the kth year respectively; I(·) is the indicator function, T h -T f When (k)>0, it is recorded as 1, otherwise it is recorded as 0; N1 and N2 represent the start and end years of the study period respectively; According to the socioeconomic exposure under the multiple models, the socioeconomic exposure under future climate change scenarios is obtained.

2. The method according to claim 1, characterized in that The threshold is obtained in the following manner: The daily precipitation after correction in each global climate model historical period is ranked, and the daily precipitation intensity exceeding the 99th percentile is selected as the threshold of extreme precipitation; The corrected daily horizontal wind speeds for each GCM historical period were ranked, and the daily wind speeds exceeding the 99% quantile were selected as the threshold for extreme winds.

3. The method according to claim 1, characterized in that The above thresholds define the combined rainstorm and wind events in the historical and future periods of the global climate model, including: Based on the precipitation thresholds of the historical periods of the global climate models, the precipitation days exceeding the precipitation thresholds in the historical period are selected as extreme precipitation events, and then the events with a continuous occurrence time exceeding a first preset number of days are selected from the extreme precipitation events as rainstorm events; Based on the high wind thresholds of each global climate model historical period, events exceeding the high wind thresholds in the historical period are selected as extreme high wind events; When the extreme high wind event occurs during the rainstorm event within a preset time period, it is confirmed that a combined rainstorm and high wind disaster has occurred.

4. The method according to claim 1, characterized in that: The method for obtaining the socioeconomic exposure under future climate change scenarios is the arithmetic average method.

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