CN115238947A - Social and economic exposure degree estimation method for drought, waterlogging and sudden turning event under climate change - Google Patents
Social and economic exposure degree estimation method for drought, waterlogging and sudden turning event under climate change Download PDFInfo
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Abstract
The application discloses a socioeconomic exposure degree estimation method for drought, waterlogging and sudden turning events under climate change. In the technical scheme, the latest global climate mode set and multivariate deviation correction are combined, the meteorological hydrographic process of future scenes is simulated, the riverway submerging water depth is simulated through a distributed hydrographic model and a CaMa-Flood hydrodynamic model, the future land water reserves and drought events are simulated through a land mode, the dynamic population and GDP scenes sharing social economic paths are further considered, and therefore the socioeconomic exposure degree of drought and Flood rush-turn events influenced by climate change is scientifically evaluated. The method has important scientific significance for risk prediction and loss evaluation of drought and flood disasters in the future, and provides certain theoretical reference and technical basis for further evaluation of environmental and disaster effects caused by global system evolution.
Description
Technical Field
The application relates to the technical field of climate response evaluation, in particular to a socioeconomic exposure degree estimation method for drought, waterlogging and sudden turning events under climate change.
Background
Global climate change changes the energy balance and water circulation process of a land-atmosphere system, and extreme climate disasters such as high temperature heat wave, drought, flood and the like are frequent, so that great challenges are brought to the sustainable development of social and economic systems and ecological environments. China is one of the most seriously affected areas by flood and drought disasters, the climate increase Wen Sulv is far higher than the global average level, and the temperature rises by 4 ℃ at the end of the century or at the end of the century, so that the flood control safety, the water supply safety, the food safety, the energy safety and the ecological environment safety of China are seriously threatened. The social and economic influences of drought and waterlogging disasters under the climate change situation are deeply understood, and the method has important significance for predicting the drought and waterlogging risks, preventing and reducing disasters and adapting to management in the future.
In recent years, scholars at home and abroad combine global climate pattern Sets (GCMs) and watershed hydrological models to study the evolution law of future flood disasters, and some scholars also combine GRACE gravity satellites and GCMs to study the influence of drought events on the social economic system under climate change, but only a few documents quantitatively evaluate the influence of drought and flood sudden-turn events under the climate change situation on the social economic system. Meanwhile, the method is limited by the fact that future socioeconomic development scenes are difficult to predict, the existing literature generally assumes that future population and GDP data are unchanged with a certain level of a historical period, dynamic development characteristics of the future socioeconomic are ignored, objective rules of social operation are not met, and rationality and scientificity of assessment of the socioeconomic influence of drought and flood disasters are restricted.
Disclosure of Invention
In view of the above, the method for estimating the socioeconomic exposure of the drought, waterlogging and sudden turning event under the climate change can effectively and accurately estimate the socioeconomic exposure of the drought, waterlogging and sudden turning event under the climate change.
The application provides a socioeconomic exposure degree estimation method for drought, waterlogging and sudden turning events under climate change, which comprises the following steps:
acquiring output data of a global climate mode set, and acquiring a distributed hydrological model aiming at a target area to be estimated;
correcting the output data of the set of global climate modes;
driving a hydrodynamic model and the distributed hydrological model based on the corrected output data to obtain the grid submerging water depth under the condition of simulating climate change;
driving a global hydrological model based on the corrected output data to obtain land water reserve abnormal data under the climate change;
calculating a drought characteristic value under the climate change based on the land water reserve abnormal series;
acquiring drought and flood rush turning events of the target area in a historical period and a future period according to the grid submerging water depth and the drought characteristic value, and extracting drought strength and flood submerging strength in the drought and flood rush turning events;
and constructing a time-varying most probable weight function about drought intensity and flood inundation intensity in the drought and flood rush turning event, and obtaining socioeconomic exposure caused by the increase of the risk of the drought and flood rush turning in the future on the basis of the time-varying most probable weight function.
Optionally, the "acquiring a distributed hydrological model for a target area to be predicted" includes:
acquiring basic data, wherein the basic data comprises long-series meteorological and runoff data, satellite observation data and digital elevation model data of a target area;
and carrying out calibration on the distributed hydrological model of the target area through the VIC model based on the basic data.
Optionally, the correction is a multivariate deviation correction method.
Optionally, the "correcting the output data of the set of global climate modes" comprises:
correcting the deviation of the variable of the output data on each quantile by adopting a quantile mapping method;
and rebuilding the correlation relation between the variables.
Optionally, the reconstruction method is a free distribution method.
Optionally, the extracting the drought characteristic value is extracting the duration and intensity of the drought through a run-length theory.
Optionally, the constructing the time-varying most probable weight function is constructed by a Copula function.
The method for estimating the socioeconomic exposure degree of the drought and Flood jerk event under the climate change combines the latest global climate mode set and multivariate deviation correction to simulate the meteorological hydrological process of the future situation, simulate the riverway submergence depth through a distributed hydrological model and a CaMa-Flood hydrodynamic model, simulate the future land water storage capacity and the drought event through a land model, and further consider the dynamic population and GDP situation of the shared socioeconomic path, thereby scientifically estimating the socioeconomic exposure degree of the drought and Flood jerk event under the climate change influence. The method has important scientific significance for risk prediction and loss evaluation of drought and flood disasters in the future, and provides certain theoretical reference and technical basis for further evaluation of environmental and disaster effects caused by global system evolution.
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The technical solution and other advantages of the present application will become apparent from the detailed description of the embodiments of the present application with reference to the accompanying drawings.
FIG. 1 is a flowchart illustrating an embodiment of a prediction method;
FIG. 2 is a schematic diagram of probability density functions of the maximum air temperatures before and after correction in the historical time period according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
According to the method for estimating the socioeconomic exposure degree of the drought and flood sudden-turning event under the climate change, hydrological, meteorological and underlying surface data of a target area are collected, a distributed basin hydrological model is calibrated, and output data of a global climate mode set are extracted; then carrying out deviation correction on the GCMs data based on a multivariable deviation correction method to obtain meteorological series of a historical period and a future period; driving a distributed hydrological model and a hydrodynamic model based on the corrected meteorological data, and simulating the submerged depth of the river channel under the climate change situation; driving a global hydrological model based on the corrected meteorological data, simulating a future land water reserve series, calculating a TWSA-DSI index under the climate change, and extracting a corresponding drought intensity value through a run length theory; further identifying drought and flood sudden-turn events of the target area in a historical period and a future period, and extracting drought strength and flood inundation strength in the events; and finally, constructing a time-varying most probable right function of each grid drought and waterlogging rush turning event based on a Copula function, and evaluating the social and economic exposure caused by the increase of the drought and waterlogging rush turning risk in the future, wherein the detailed flow is shown in fig. 1.
The technical scheme of the present application is further specifically described below by way of examples and with reference to the accompanying drawings:
step 1, collecting hydrological, meteorological and underlying surface data of a target area, calibrating a distributed watershed hydrological model, and extracting output data of a global climate mode set;
step 1 further comprises the following substeps:
step 1.1, collecting long-series meteorological data and actual measurement radial data of a target area, collecting observation data of GRACE and GRACE-FO satellites, and meanwhile collecting basic data such as DEM (Digital Elevation Model) data, vegetation, soil, river network of a drainage basin and the like.
And step 1.2, calibrating the distributed watershed hydrological model by using the measured data.
In this embodiment, based on the basic data in step 1.1, the VIC model is used as a distributed hydrological model development calibration of the target area. Compared with a lumped hydrological model, the distributed hydrological model can better reflect the spatial distribution of soil and vegetation, the regional characteristics of land utilization and the spatial distribution of precipitation, evaporation and runoff, is established on a grid point, and is more suitable for being coupled with a global climate mode.
Step 1.3, three representative concentration paths are selected, and M Global Climate Modes (GCMs) data of CMIP6 (the sixth international coupling mode comparison plan) are extracted.
Because a single GCM model has large uncertainty, M Global Climate Modes (GCMs) are adopted for outputting data. Further, the embodiment selects three scenes of a history period and a future period, wherein the history period is 1985-2014, and the future period is 2015-2100; the three scenes in the future are SSP126, SSP245 and SSP585, and the extracted GCMs are daily precipitation, daily maximum air temperature, daily minimum air temperature, daily average air temperature, near-earth wind speed, relative humidity, specific humidity, snow precipitation, short wave radiation, long wave radiation and air pressure.
And 2, carrying out deviation correction on the GCMs data based on a multivariable deviation correction method to obtain weather series of a historical period and a future period.
Step 2 further comprises the following substeps:
and 2.1, correcting the deviation of the daily precipitation, the daily maximum air temperature and the minimum air temperature output by the GCMs on each quantile by adopting a quantile mapping method. Specifically, the difference value of each quantile (0.01-0.99) between the GCMs output variable and the observed meteorological variable is calculated, and the difference value is removed from each quantile output by the future GCMs, so that the future corrected GCMs climate prediction is obtained.
Wherein, the correction to temperature and precipitation respectively as follows:
T adj,d ＝T GCM,d +(T obs,Q -T GCM，ref,Q )
P adj,d ＝P GCM,d ×(P obs,Q /P GCM,ref,Q ) (1)
in the formula: t and P respectively represent air temperature and rainfall, adj represents a corrected sequence, obs represents observation data, ref and fut respectively represent a historical reference period and a future prediction period, d represents day data, Q represents quantiles, and T adj,d Indicating the corrected solar temperature, T GCM,d Indicating the daily air temperature, T, of the GCM output obs,Q Representing the quantile of air temperature observation data, T GCM，ref,Q Representing the quantile, P, of the temperature observation data output by the GCM during the historical reference period adj,d Indicating the corrected daily precipitation, P GCM,d Indicating the daily precipitation output of GCM, P obs,Q Water dropping indicationQuantile of data, P GCM,ref,Q Representing quantiles of precipitation observation data output by the GCM in a historical reference period;
2.2, rebuilding the correlation relation among the air temperature precipitation variables corrected in the step 2.1;
as the research data shows that the GCMs output has certain deviation on the single variable quantile and also has analog deviation on the correlation structure among the variables, the technology adopts a free Distribution-free method to reconstruct the correlation among the variables of the data obtained in the step 2.1. Firstly, calculating Van der Waals values of GCMs output data corrected in the step 2.1 to obtain value matrixes [ Ws, r ] and [ Ws, f ] of historical data and future data, and then respectively carrying out the Korotkoff decomposition on correlation coefficient matrixes among variables of observation data and GCMs output data (including history and future):
in the formula: [ Co, r ]]A matrix of correlation coefficients, [ Cs, r ] representing historical observation data]And [ Cs, f ]]Matrix of correlation coefficients, P, representing historical and future GCMs output data, respectively o,r Is [ Co, r ]]Triangular matrix obtained after decomposition, P s,r Is C s,r Triangular matrix obtained after decomposition, P s,f Is C s,f The triangular matrix obtained after decomposition is passed
And calculating to obtain an adjusted score matrix. In order to keep precipitation as a key input variable influencing runoff simulation, the reconstructed precipitation air temperature combination is integrally adjusted (the corrected air temperature sequence order is adjusted) by taking the precipitation order obtained in the step 2.1 as a reference so as to ensure that
And
and obtaining the GCMs correction data after the correlation is reconstructed.
As shown in fig. 2, a schematic diagram of a probability density function for correcting the maximum air temperatures at the previous and subsequent historical days by this method is shown.
And 2.3, correcting the daily average air temperature, the near-earth wind speed, the relative humidity, the specific humidity, the snowfall amount, the short wave radiation, the long wave radiation and the air pressure by adopting a quantile mapping method.
Since the correlation between the variable and the precipitation is difficult to quantify, the variable is corrected by only adopting the correction method for the air temperature in the formula (1), and the secondary correction is not performed by considering the correlation with the precipitation.
Step 3, driving a distributed hydrological model and a hydrodynamic model based on the corrected meteorological data, and simulating the submerged depth of the river channel under the climate change situation;
step 3 further comprises the following substeps:
step 3.1, driving a VIC model by adopting a weather series under the weather change situation, and outputting the rasterized runoff depth of the target area;
and 3.2, adopting the runoff depth driving hydrodynamic model in the step 3.1 to obtain the river channel submerging depth under the climate change situation.
Further, the hydrodynamic model adopted in this embodiment is a CaMa-Flood model, which is suitable for being combined with a distributed hydrological model or a land process model, and the model assumes that each grid includes a channel-type reservoir and a Flood area reservoir, and simulates a Flood flooding situation mainly through momentum conservation and water balance.
Step 4, driving a global hydrological model based on the corrected meteorological data, simulating a future land water reserve series, and correcting the simulated series by taking the gravity satellite observation data as a reference to obtain land water reserve abnormal data under climate change;
step 4.1, corrected GCMs data are used as input, a CLM4.5 land mode is driven, and land water reserve series under climate change are estimated;
CLM4.5 is a land module of the universal earth system model, and compared with the earlier version, the parameterization scheme of version 4.5 is greatly improved, and can describe various aspects of land processes, including surface heterogeneity, biogeophysical processes, hydrologic cycles, biogeochemical processes, anthropogenic influence, ecosystem dynamic processes and the like. And (4) sequentially inputting the GCMs meteorological data corrected in the step (2) into a CLM4.5 mode, and estimating to obtain land water storage quantity series of a historical period and a future period.
Further, driving the CLM4.5 mode to predict future land water reserves is conventional in the art and will not be described herein.
Step 4.2, dividing dry/wet half-year of each grid based on the TWSA (terrestrial water storage analog, TWSA) data observed by the GRACE/GRACE-FO satellite, wherein the dry half-year and the wet half-year respectively account for 6 months;
and 4.3, selecting a distance period identical to the satellite data of the GRACE/GRACE-FO, and deducting the mean value of the series during 2004-2009 from the land water storage series simulated by the CLM4.5 mode for each earth system mode under each SSP path so as to obtain the monthly TWSA series of the historical period and the future period.
Step 4.4, correcting the TWSA series simulated by the CLM4.5 mode;
first, the TWSA observed for GRACE/GRACE-FO (noted as TWSA) is calculated separately
) TWSA (recorded as TWSA) simulated in the historical period of CLM4.5 model simulation (1985 to 2014)
) TWSA simulated in the scenario period (2015-2100) of CLM4.5 mode simulation (described as TWSA
) The trend line is translated to make the sum of the trend terms of each year be 0; for series whose annual trend is not significant at the 0.05 confidence level, resetting the annual trend term to 0; then, removing the trend items of each series to obtainThree sets of new series
And
thereafter, the history periods under different quantiles are calculated
And a scene period
And based on true observations
Obtaining pseudo-observation series of contextual periods
In the formula: p is different quantiles, and 50 equidistant quantiles are selected within the range of 0.01-0.99.
According to
Simulation series for scene period
The frequency distribution curve is subjected to deviation correction, a corrected future series is obtained based on quantile mapping, and a corrected future scene is finally obtained by adding a trend term obtained by first-step deduction; the application considers that the frequency distribution function of TWSA may have certain difference under dry and wet conditions of the land, so the method is based on GRACE/GRACE-FO satellites from 2002 to 2020TWSA data divides the dry/wet half-year of each grid and performs corrections for the two half-years divided in step 4.1.
Step 5, calculating TWSA-DSI indexes under climate change, and extracting corresponding drought intensity values through a run-length theory;
5.1 calculating the drought index under the climate change based on the series of land water reserve abnormality;
the TWSA-DSI index is used for measuring the dry and wet degree of the land, and is a dimensionless standardized water storage capacity abnormity index which has space comparability among different hydrological climate areas. Negative values of TWSA-DSI indicate land water reserves below the mean level for the study period, and are used to characterize drought levels; similarly, positive values may be used to measure the level of land wetness. The formula for calculating the TWSA-DSI series is as follows:
in the formula: TWSA i,j TWSA data representing the jth month of the ith year,
and σ j Mean and standard deviation of TWSA at month j during the study period, respectively.
According to the application, the mean value and the standard deviation of each month of TWSA are calculated by selecting a series from 1985 to 2100 years, and the dry and wet strength of the land is divided into different grades based on TWSA-DSI indexes, which are detailed in the following table 1:
TABLE 1 Dry and Wet rating Classification Standard based on TWSA-DSI index
5.2 extracting the drought intensity under the climate change based on the run-length theory;
for global climate mode data under each SSP path, the method in step 5.1 is adopted to calculate TWSA-DSI index under climate change; and then, on the basis of the run-length theory, respectively extracting the drought intensity characteristic values of the historical period and the future period by taking-0.8 as a threshold value.
Step 6, identifying drought and flood sudden turning events of the target area in a historical period and a future period, and extracting drought strength and flood submerging strength in the events;
the step 6 specifically comprises the following steps:
step 6.1, based on the submerging water depth of each grid obtained in the step 3, assuming that the flood control standard is unchanged, extracting flood events exceeding the flood control standard;
step 6.2, based on the drought event obtained in the step 5.1, when an over-standard flood event occurs within 10 days after the drought event occurs, defining the drought and flood rush turning event; and (3) representing the flood intensity of the drought and waterlogging rush transfer event by adopting the highest submergence depth in the over-standard flood event within 10 days after the drought event occurs, and representing the drought intensity of the drought and waterlogging rush transfer event by adopting the drought intensity.
And 7, constructing a time-varying most probable right function of each grid drought and waterlogging rush turning event based on the Copula function, and evaluating the social and economic exposure caused by the increase of the drought and waterlogging rush turning risk in the future.
Step 7.1, constructing a time-varying most probable weight function of drought intensity and flood intensity in each gate drought and flood sudden-turn event under the non-consistency condition based on the recurrence period;
the joint probability distribution of the drought strength and the flood strength is constructed through a Copula function. Unlike univariate frequency analysis, the joint recurrence period of two variables is composed of contour lines, on which an infinite number of combinations are included, and how to determine a reasonable combination scenario is critical to risk quantification. In the past, the joint design value is mostly selected based on the same frequency hypothesis in the research of the composite disaster event, the statistical basis is lacked, and the objective occurrence rule of the drought event is difficult to capture. The method includes the steps that a maximum possible weight function is constructed based on a recurrence period, and a combined scene of drought-waterlogging and sudden-turning events is optimized based on a joint probability density function maximum principle:
in the formula: c [ F ] HW ,F DR ]Probability of being CopulaA density function; f. of HW 、f DR Probability density function characterizing drought intensity, flood intensity, F HW And F DR Then it is a cumulative distribution function; (d, s) represents the most likely combined scenario of drought intensity HW and flooding intensity DR under a certain joint recurrence period tan; and E is the average interval time of drought, waterlogging and sudden turning events.
7.2, calculating the socioeconomic exposure degree caused by the increased risk of the drought and flood sudden turning events of each grid under the future climate change;
in order to quantitatively describe the potential socioeconomic exposure caused by the increase of the risk of the drought and flood events in the future, firstly, solving a formula (2) by adopting a Newton iteration method to obtain the drought strength and the flood strength (Dh, sh) corresponding to a certain recurrence period (Th) in a history period (1985-2014), and then, constructing the time-varying edge distribution and the Copula function in the future period (2015-2100 years) by taking 30 years as a sliding window (which is consistent with the history period); sequentially substituting Dh and Sh into the time-varying distribution function of the kth sliding window in the future time period, and calculating to obtain a new reappearance period T f (k) (ii) a If T f (k)<T h Indicating that the risk of drought, waterlogging and sudden turning events of the kth window is increased, and otherwise, the risk is reduced. For the kth time window, parameters of edge distribution and joint distribution are calculated by adopting data of 15 years before and after the central point, and the socioeconomic exposure degree of the future period is measured by the following formula:
in the formula: e pop And E GDP Respectively characterizing population affected by drought, waterlogging and sudden turning risk increase and GDP exposure, POP k And GDP k Population and GDP for the k year, respectively; i (-) is an indicator function, T h -T f (k)>When 0, the value is 1, otherwise, 0 is taken; n is a radical of 1 And N 2 The beginning and end years of the study period were characterized separately.
In the past research, the joint distribution of history and future periods is usually directly constructed, the socioeconomic exposure degree of the future periods is defined as 0 or 100% by comparing the joint recurrence period changes of the two periods, the method cannot capture the annual change characteristics of two-dimensional extreme event risks and the dynamic attributes of socioeconomic indexes, and certain unreasonableness exists. According to the method and the system, the inconsistency possibly caused by future climate change can be considered, the annual characteristics of risk change can be mined, and the dynamic data of the shared socioeconomic path are fully utilized.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application.
Claims (7)
1. A socioeconomic exposure estimation method for drought, waterlogging and sudden-turn events under climate change is characterized by comprising the following steps:
acquiring output data of a global climate mode set, and acquiring a distributed hydrological model aiming at a target area to be estimated;
correcting the output data of the set of global climate modes;
driving a hydrodynamic model and the distributed hydrological model based on the corrected output data to obtain the grid submerging water depth under the situation of simulating climate change;
driving a global hydrological model based on the corrected output data to obtain land water reserve abnormal data under the climate change;
calculating a drought characteristic value under the climate change based on the land water reserve abnormal series;
acquiring drought and flood rush turning events of the target area in a historical period and a future period according to the grid submerging water depth and the drought characteristic value, and extracting drought strength and flood submerging strength in the drought and flood rush turning events;
and constructing a time-varying most probable weight function about drought intensity and flood inundation intensity in the drought and flood rush turning event, and obtaining socioeconomic exposure caused by the increase of the risk of the drought and flood rush turning in the future on the basis of the time-varying most probable weight function.
2. The method of claim 1, wherein the obtaining the distributed hydrological model for the target area to be predicted comprises:
acquiring basic data, wherein the basic data comprises long-series meteorological and runoff data, satellite observation data and digital elevation model data of a target area;
and carrying out calibration on the distributed hydrological model of the target area through the VIC model based on the basic data.
3. The method of claim 1, wherein the correction is a multivariate bias correction method.
4. The method of claim 1, wherein the correcting the output data of the set of global climate modes comprises:
correcting the deviation of the variable of the output data on each quantile by adopting a quantile mapping method;
and rebuilding the correlation relation between the variables.
5. The method of claim 4, wherein the reconstruction is by a free distribution method.
6. The method of claim 1, wherein the extracting the drought characteristic value is extracting the duration and intensity of the drought through a run-length theory.
7. The method of claim 1, wherein the constructing the time-varying most probable weight function is by a Copula function.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116150548A (en) * | 2023-04-17 | 2023-05-23 | 云南省水利水电科学研究院 | River flood inundation range calculation method |
| CN116522763A (en) * | 2023-04-17 | 2023-08-01 | 华中科技大学 | Hot wave-drought composite disaster assessment method and system |
| CN117057490A (en) * | 2023-10-12 | 2023-11-14 | 武汉大学 | Prediction method and system for wet stress heat wave-flood composite disaster and electronic equipment |
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2022
- 2022-05-20 CN CN202210558666.3A patent/CN115238947A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116150548A (en) * | 2023-04-17 | 2023-05-23 | 云南省水利水电科学研究院 | River flood inundation range calculation method |
| CN116150548B (en) * | 2023-04-17 | 2023-07-21 | 云南省水利水电科学研究院 | River flood inundation range calculation method |
| CN116522763A (en) * | 2023-04-17 | 2023-08-01 | 华中科技大学 | Hot wave-drought composite disaster assessment method and system |
| CN116522763B (en) * | 2023-04-17 | 2023-12-19 | 华中科技大学 | Hot wave-drought composite disaster assessment method and system |
| CN117057490A (en) * | 2023-10-12 | 2023-11-14 | 武汉大学 | Prediction method and system for wet stress heat wave-flood composite disaster and electronic equipment |
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