A method for assessing socioeconomic exposure to two-dimensional drought under non-uniform conditions

Abstract

The present application discloses a method for assessing the socioeconomic exposure of two-dimensional drought under non-consistent conditions. In this technical solution, taking terrestrial water storage as a metric and considering the two-dimensional properties of drought events, the observation data of the GRACE / GRACE‑FO satellites are first obtained, and the three types of shared socioeconomic paths of low forcing, medium forcing and high forcing are considered to obtain the output data of the Earth system model and the population and GDP data; then, the Trend‑preserving bias correction method based on quantile mapping is used to estimate the abnormal terrestrial water storage data under climate change; further, considering the non-consistent characteristics of the changing environment, a time-varying Copula function of drought duration and intensity is constructed, and the maximum possible weight function is used to analyze the future changes in two-dimensional drought risks; finally, based on the shared socioeconomic path, the population and GDP exposure of the areas with increased drought risk are assessed. The assessment method of this application is not only scientific and reasonable, close to engineering practice, but also can provide engineering reference value for responding to future drought 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 assessing socioeconomic exposure to two-dimensional drought under non-uniform conditions. Background Art

[0002] Since the Industrial Revolution, the emission of greenhouse gases such as carbon dioxide in the atmosphere has increased, the global land temperature has continued to rise, coral reefs and tropical rainforests have died out on a large scale, extreme disasters such as droughts have occurred frequently, and the imbalance and inequality of the social and economic systems and ecological environment have become increasingly severe. Drought events have complex causes, a long time span, and strong destructive power. They are important factors restricting the sustainable development of natural ecosystems and social economy. They are often divided into meteorological drought, hydrological drought, agricultural drought, and socioeconomic drought. Among them, meteorological drought and hydrological drought have a significant impact on water resources management and various water-related activities, and are important categories of drought events that have attracted much attention. Meteorological drought mainly refers to the phenomenon of less precipitation, which is mostly caused by abnormal atmospheric circulation; meteorological drought is the cause of hydrological drought. Less precipitation and higher temperatures may cause soil water, river and lake runoff, and groundwater drought, which further triggers hydrological drought. Severe drought events are often the result of the gradual development of meteorological drought and hydrological drought. There are many factors affecting drought, including hydrology, meteorology, and vegetation, and they are often closely correlated. Therefore, in order to quantitatively characterize the degree of water deficit, domestic and foreign scholars have proposed a large number of single-factor and multi-factor comprehensive drought indices, such as the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Palmer Drought Index, and Standardized Runoff Index. Although the research objects and physical processes of each drought index are different, they mainly consider one or more meteorological and hydrological elements such as precipitation, evapotranspiration, runoff, and soil moisture, and cannot fully characterize the intrinsic physical characteristics of drought events.

[0003] In March 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite was successfully launched, providing a continuous, high-precision direct observation method for obtaining global large-scale migration of Earth's surface materials. The gravity field model based on the GRACE satellite signal solution can extract the change information of the Earth's lunar gravity field at a spatial scale of 300km×300km, deduct the influence of factors such as crustal material movement, atmospheric movement, ocean currents and tides, and can effectively reflect the gravity changes caused by ice and snow, surface water, soil water, groundwater and human factors, and comprehensively monitor the terrestrial water storage anomaly (TWSA) signal. In May 2018, after the GRACE gravity satellite stopped working for a year, the GRACE-FO (GRACE Follow-On) gravity satellite was successfully launched, continuing the scientific observation mission of the GRACE satellite. The GRACE / GRACE-FO gravity satellite effectively solved the problems of shallow ground observation range, uneven spatial distribution, and difficulty in data acquisition, and showed great potential in tracking regional and global drought events. Some scholars proposed a drought severity index based on GRACE terrestrial water storage anomalies (TWSA-based drought severity index, TWSA-DSI) and found that TWSA-DSI can monitor global and regional drought events more effectively than traditional drought indicators.

[0004] The world is currently experiencing climate change dominated by "warming". The response of precipitation to climate warming has significant temporal and spatial heterogeneity, and its changes are affected by atmospheric water vapor capacity, relative humidity and atmospheric stability. Land water storage is affected by the synergistic effect of precipitation and evapotranspiration, and its response to global warming is more complex. It is not clear how drought will evolve in the future. Although GRACE / GRACE-FO satellites have begun to be used for drought monitoring and assessment, relevant research on predicting future drought based on land water storage anomalies at home and abroad has just started; at the same time, these documents usually only focus on changes in single characteristic attributes such as drought duration or intensity, and do not consider the inconsistency characteristics of drought. It is difficult to accurately characterize the multivariate characteristic attributes of drought events and cannot effectively reflect future changes in drought risks. At present, it has become a difficult problem to scientifically quantify multidimensional drought events under future inconsistency conditions. There is no literature report on how to quantitatively assess the potential impact of changes in drought risk on the socio-economic system. Summary of the invention

[0005] In view of this, this application provides a socioeconomic exposure assessment method for two-dimensional drought under non-uniform conditions, which can effectively and accurately characterize the changing characteristics of future drought conditions under climate change.

[0006] A method for assessing socioeconomic exposure to two-dimensional drought under non-uniform conditions, the method comprising:

[0007] Access to gravity satellite observations, Earth system model outputs, and shared socioeconomic data;

[0008] Drive the land surface process model according to the output data of the earth system model to obtain the terrestrial water storage under climate change;

[0009] Correcting the terrestrial water reserves to obtain terrestrial water reserves abnormal data;

[0010] Derived the TWSA-DSI index under climate change and extracted the corresponding drought characteristic values;

[0011] The most likely weight function for the drought characteristic value is constructed to assess the socioeconomic exposure caused by increased drought risk in the future.

[0012] Optionally, the gravity satellite observation data are observation data of GRACE and GRACE-FO satellites.

[0013] Optionally, the shared economic socio-economic data includes population data and GDP data.

[0014] Optionally, "driving the land surface process model" is by driving the CLM4.5 land surface model.

[0015] Optionally, the correction is performed by using a Trend-preserving deviation correction method based on quantile mapping.

[0016] Optionally, the step of extracting drought characteristic values ​​is to extract drought duration and severity through run theory.

[0017] Optionally, the constructing of the most likely weight function is to construct a time-varying most likely function of drought duration and intensity under inconsistent conditions based on a Copula function.

[0018] Taking terrestrial water storage as a metric and considering the two-dimensional attributes of drought events, we first obtain observation data from GRACE / GRACE-FO satellites, consider three types of shared socioeconomic pathways, namely low forcing, moderate forcing and high forcing, and obtain Earth system model output data as well as population and GDP data. Then, we use the Trend-preserving bias correction method based on quantile mapping to estimate terrestrial water storage anomaly data under climate change. Furthermore, considering the inconsistency characteristics of the changing environment, we construct a time-varying Copula function of drought duration and intensity, and use the maximum possible weight function to analyze future changes in two-dimensional drought risk. Finally, based on the shared socioeconomic pathway, we assess the population and GDP exposure in areas with increased drought risk.

[0019] Therefore, this application has the following beneficial effects:

[0020] 1. Scientific and reasonable, close to engineering practice;

[0021] The present invention uses terrestrial water storage anomalies to measure drought characteristics, and fully considers the inconsistency characteristics of the hydrological series under the influence of climate change, constructs a time-varying Copula function of drought duration and intensity, and uses the maximum possible weight function to analyze future two-dimensional drought risk changes. It has strong physical significance and statistical basis, and can effectively characterize the changing characteristics of future droughts under climate change.

[0022] 2. It can provide engineering reference value for coping with future drought disasters and formulating scientific emission reduction strategies

[0023] The present invention combines the earth system model, land surface model and multivariate analysis method, which can provide an important and highly operational reference basis for basin drought assessment and early warning under a changing environment, and provide engineering reference value for responding to future drought disasters and formulating scientific emission reduction strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] 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.

[0025] Figure 1 Provide a specific flow chart of the evaluation method for the embodiment of the present application;

[0026] Figure 2 A schematic diagram of the starting month of China's wet half year based on GRACE / GRACE FO satellite observation data is provided for the embodiment of the present application;

[0027] Figure 3 It is a schematic diagram of the run-length theory involved in the embodiment of the present application. DETAILED DESCRIPTION

[0028] 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.

[0029] 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.

[0030] The present invention uses terrestrial water storage as a metric and considers the two-dimensional attributes of drought events. First, the observation data of GRACE / GRACE-FO satellites are obtained, and three types of shared socioeconomic paths, namely low forcing, medium forcing and high forcing, are considered to obtain the output data of the Earth system model and the population and GDP data. Then, the Trend-preserving bias correction method based on quantile mapping is used to estimate the abnormal terrestrial water storage data under climate change. Furthermore, considering the inconsistency characteristics of the changing environment, a time-varying Copula function of drought duration and intensity is constructed, and the maximum possible weight function is used to analyze the future changes in two-dimensional drought risks. Finally, based on the shared socioeconomic path, the population and GDP exposure of the areas with increased drought risk are evaluated, thereby proposing a socioeconomic exposure evaluation method for two-dimensional drought under inconsistency conditions. The specific process is described in detail in Figure 1 .

[0031] The technical solution of the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings:

[0032] The present invention provides a method for assessing watershed hydrological drought under a climate change scenario, comprising the following steps:

[0033] Step 1: Collect observation data from GRACE and GRACE-FO satellites, collect Earth system model outputs under three shared socioeconomic pathways, and obtain population and GDP data for the shared socioeconomic pathway dataset; specifically, the following steps are included:

[0034] 1.1 Obtain three different sets of GRACE / GRACE-FO satellite observation data and fill in the missing values ​​using linear interpolation;

[0035] Satellite gravity data is used to invert terrestrial water storage. Internationally, the main institutions responsible for data interpretation include the Potsdam Geoscience Center in Germany, the Jet Propulsion Laboratory (JPL) of the California Institute of Technology, the Center for Space Research at University of Texas, Austin (CSR) and the Goddard Space Flight Center (GSFC) of NASA, and the like, which publish global gravity field output data on a monthly scale. The present invention simultaneously uses the latest sixth-generation (RL06) products released by JPL, CSR and GSFC. These three data sources have different spatial resolutions and all provide equivalent water heights on a monthly scale (minus the average field from 2004 to 2009) based on the solution of the concentrated mass gravity field, and finally output a long sequence of terrestrial water storage anomaly (TWSA) data sets. Since there were 20 months of missing data due to power shortage or satellite vibration during GRACE gravity satellite measurements, and there was a one-year missing period between GRACE and GRACE-FO satellites, linear interpolation was used to fill in the missing values.

[0036] 1.2 The three GRACE / GRACE-FO gravity satellite datasets were interpolated to a 0.5° × 0.5° spatial grid to make the spatial resolution of the TWSA data consistent with that of the shared socio-economic pathways dataset. The average of each product was taken in each time step to finally obtain the TWSA monthly dataset.

[0037] 1.3 Collect meteorological data after calibration of the Earth System Model under the three shared socioeconomic pathways;

[0038] To estimate future climate scenarios, eight global climate models (GCMs) released recently by the International Coupled Model Inter-comparison Project Phase 6 (CMIP6) were used. CMIP6 uses the matrix framework of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The spatial resolution of GCMs output data is generally low, and there are large systematic deviations compared with the measured data. The international ISIMIP3b framework corrected the output data of five earth system models in GCMs. The data set takes into account the intrinsic correlation of various meteorological variables during the correction process, has a unified spatial resolution (0.5°×0.5°), and includes five earth system models, namely GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The present invention uses all meteorological data series of three scenarios (SSP126, SSP370 and SSP585) and five models of the ISMIP3b data set, setting the historical period to 1985-2014 and the future period to 2071-2100.

[0039] 1.4 Obtain population and GDP data from the Shared Socio-Economic Pathways dataset;

[0040] In order to assess the socio-economic risks that drought events may cause, the population and GDP data of three shared socio-economic 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, SSP245 and SSP585, are used. There are many international institutions that provide population and GDP simulation data of shared socio-economic pathways. The present invention uses the estimated data set under the "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 socio-economic index from 2010 to 2100 based on the Cobb-Douglas model and the population-development-environment analysis model. This data set is one of the data sets that is closest to my country's national conditions today and is widely used in the socio-economic risk assessment of extreme hydrological events.

[0041] Step 2: Use the calibrated Earth System Model data as input to drive the CLM4.5 land surface model and estimate the terrestrial water storage series under climate change;

[0042] CLM4.5 is the land surface module of the General Earth System Model. Compared with previous versions, the parameterization scheme of version 4.5 has been greatly improved, which can describe various aspects of land surface processes, including surface heterogeneity, biogeophysical processes, hydrological cycle, biogeochemical processes, human impacts and ecosystem dynamic processes. The corrected Earth System Model data under the three shared socioeconomic pathways obtained in step 1.3 are input into the CLM4.5 model in turn to estimate the land water storage series in the historical period and future period.

[0043] Furthermore, driving the CLM4.5 model to estimate future terrestrial water reserves is a conventional technique in the art and will not be elaborated herein.

[0044] Step 3: Use the trend-preserving deviation correction method based on quantile mapping to correct the terrestrial water storage simulated by the CLM4.5 land surface model to obtain abnormal terrestrial water storage data under climate change; specifically, the following steps are included:

[0045] 3.1 Based on the TWSA data observed by GRACE / GRACE-FO satellites, each grid is divided into a dry / wet half year, where the dry half year and the wet half year each account for 6 months;

[0046] like Figure 2 As shown, a schematic diagram of the starting month of the wet half year in China based on GRACE / GRACE FO satellite observation data is given.

[0047] 3.2 Select the same anomaly period as the GRACE / GRACE-FO satellite data and, for each Earth system model under each SSP path, deduct the mean of the series during 2004–2009 from the terrestrial water storage series simulated by the CLM4.5 model to obtain the monthly TWSA series from 1985 to 2100.

[0048] 3.3 Correct the TWSA series simulated by the CLM4.5 model;

[0049] First, the TWSA of GRACE / GRACE-FO observations (denoted as ), TWSA (denoted as ), TWSA (denoted as ), and then translate the trend line so that the sum of the trend items of each year is 0; for the series whose interannual trend is not significant at the 0.05 confidence level, reset the trend items of each year to 0; then, remove the trend items of each series to obtain three new series and After that, 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

[0050]

[0051] Where: p is the different quantiles set, and 50 equally spaced quantiles are taken in the range of 0.01 to 0.99.

[0052] 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. The present invention takes into account that the frequency distribution function of TWSA under dry and wet conditions on land may be different. Therefore, the TWSA data of GRACE / GRACE-FO satellites from 2002 to 2020 are used to divide each grid into dry / wet half years, and correction is performed on the two half years divided in step 3.1 respectively.

[0053] Step 4: derive the TWSA-DSI index under climate change, and then extract the corresponding drought characteristic values, including drought duration and intensity, through the run theory;

[0054] 4.1 Derivation of drought index under climate change based on terrestrial water storage anomaly series;

[0055] The TWSA-DSI index is used to measure the dryness and wetness of land. It is a dimensionless standardized water storage anomaly index that is spatially comparable between different hydrological and climatic regions. A negative value of TWSA-DSI indicates that the land water storage is lower than the average level of the study period, which is used to characterize the degree of drought; similarly, a positive value can be used to measure the wetness of land. The calculation formula of the TWSA-DSI series is as follows:

[0056]

[0057] Where: TWSA i,j represents the TWSA data of the jth month of the i-th year, and σ j are the mean and standard deviation of TWSA in the jth month during the study period, respectively.

[0058] The present invention selects a long series from 1985 to 2100 to calculate the monthly mean and standard deviation of TWSA, and divides the land dryness and wetness intensity into different levels based on the TWSA-DSI index, as shown in Table 1:

[0059] Table 1 Dryness and wetness grading standards based on TWSA-DSI index

[0060]

[0061] 4.2 Extracting drought duration and intensity under climate change based on run theory;

[0062] For the Earth System Model data under each SSP path, the method described in step 4.1 is used to deduce the TWSA-DSI index under climate change; then, based on the run theory, the characteristic values ​​of drought duration and drought intensity in the historical period and future period are extracted respectively.

[0063] like Figure 3 As shown, a schematic diagram of run theory is given.

[0064] Step 5: Based on the Copula function, the time-varying most likely weight function of drought duration and intensity is constructed, and the socioeconomic exposure assessment method caused by the increase in future drought risk is evaluated.

[0065] Step 5.1 Construct the time-varying most likely weight function of drought duration and intensity under non-uniform conditions;

[0066] The present invention takes the TWSA-DSI index less than -0.5 as the threshold, obtains a sufficient number of drought event samples, and then identifies the characteristics of monthly-scale drought events field by field based on the run theory, and then constructs the joint probability distribution of drought duration and intensity through the Copula function. Different from univariate frequency analysis, the joint return period of two variables is composed of contours, which contain countless combinations. How to determine a reasonable combination scenario is extremely critical for risk quantification. Most previous studies on two-dimensional drought events 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. The present invention constructs the maximum possible weight function and optimizes the combination scenario of two-dimensional drought attributes based on the maximum principle of the joint probability density function:

[0067]

[0068] Where: c[F D (d),F S (s)] is the probability density function of Copula; f D (d) and (f) S (s) probability density function that characterizes the marginal distribution of drought duration and drought intensity, F D (d) and F S (s) is the cumulative distribution function; (d*, s*) represents a joint return period T or is the most likely combination scenario of drought duration d and intensity s; μ is the average interval between drought events.

[0069] Step 5.2: Estimate the socioeconomic exposure caused by increased drought risk under future climate change;

[0070] In order to quantitatively characterize the potential socioeconomic exposure caused by the increase in future two-dimensional drought risk, the Newton iteration method is first used to solve equation (3) to obtain a return period (T) in the historical period (1985-2014). h ) corresponding to the drought duration and intensity (D h ,S h ), and then use 30 years as a sliding window (consistent with the length of the historical period) to construct the time-varying marginal distribution and Copula function for the future period (2015-2100); D h and S h Substitute the time-varying distribution function of the kth sliding window in the future period in sequence and calculate the new return period T f (k); if T f (k)<T h This indicates that the two-dimensional drought risk of the kth window increases, and vice versa. For the kth time window, the marginal distribution and joint distribution parameters are derived using the 15-year data before and after the center point, and the socioeconomic exposure in the future period is measured by the following formula:

[0071]

[0072]

[0073] Where: E pop and E GDP Characterize the population and GDP exposure to the increased risk of two-dimensional drought, 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.

[0074] Previous studies usually directly constructed the joint distribution of historical and future periods, and defined 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 drought risk and the dynamic properties of socioeconomic indices, and is somewhat irrational. The present invention 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.

[0075] 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 assessing socioeconomic exposure to two-dimensional drought under non-uniform conditions. It is characterized in that The method comprises: Access to gravity satellite observations, Earth system model outputs, and shared socio-economic data; Drive the land surface process model according to the output data of the earth system model to obtain the terrestrial water storage under climate change; Correcting the terrestrial water reserves to obtain abnormal terrestrial water reserves data; including: Based on the TWSA data observed by GRACE / GRACE-FO satellites, each grid is divided into a dry / wet half year, where the dry half year and the wet half year each account for 6 months; The same anomaly period as the GRACE / GRACE-FO satellite data was selected. For each Earth system model under each SSP path, the terrestrial water storage series simulated by the CLM4.5 model was deducted from the mean of the series during 2004-2009, thus obtaining the monthly TWSA series from 1985 to 2100. Correction of the TWSA series simulated by the CLM4.5 model; Derived the TWSA-DSI index under climate change and extracted the corresponding drought characteristic values; including: Derivation of drought index under climate change based on terrestrial water storage anomaly series; Extracting drought duration and intensity under climate change based on run theory; The most likely weight function for the drought characteristic value is constructed to assess the socioeconomic exposure caused by increased drought risk in the future.

2. The method according to claim 1, It is characterized in that The gravity satellite observation data are observation data of GRACE and GRACE-FO satellites.

3. The method according to claim 1, It is characterized in that The shared socio-economic data include population data and GDP data.

4. The method according to claim 1, It is characterized in that The "driven land surface process model" is achieved by driving the CLM4.5 land surface model.

5. The method according to claim 1, It is characterized in that The correction is a trend-preserving deviation correction method based on quantile mapping.

6. The method according to claim 1, It is characterized in that The method of extracting drought characteristic values ​​is to extract drought duration and intensity through run theory.

7. The method according to claim 1, It is characterized in that The most likely weight function is constructed based on the Copula function to construct the time-varying most likely function of drought duration and intensity under inconsistent conditions.

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