CN111027175A - Method for evaluating social and economic influences of flood based on coupling model integrated simulation - Google Patents
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Abstract
The invention discloses a method for evaluating socioeconomic impact of flood. The method is based on a land hydrological model, and based on simulated climatic conditions under different future temperature rise scenes estimated by a global climatic mode, a global climatic mode-land hydrological model coupling model is established by developing numerical experiments of flood process simulation under different production convergence mechanisms and evaporation mechanisms, so as to simulate the global flood process. The method improves the space-time resolution of global flood process simulation, provides possibility for the refined evaluation and management of global flood risks, fully considers the socioeconomic development conditions of different regions under different socioeconomic development paths while considering different production convergence mechanisms and evaporation mechanisms in the flood simulation process, and accordingly, the influence of the flood risks on socioeconomic development in different regions and different time periods is more scientific and reasonable.
Description
Technical Field
The invention relates to the technical field of atmospheric science, in particular to a method for evaluating the influence of flood on social economy based on coupling model integrated simulation.
Background
Flood disasters have the characteristics of wide influence range, strong burstiness, long history, frequent occurrence, high harmfulness and strong seasonality. Along with the remarkable improvement of the comprehensive defense capacity of flood disasters, the population of the dead people of flood disasters in China is in a remarkable descending trend. However, with the increase of population and the development of social economy, the economic bearing capacity per unit area and the exposure degree of infrastructure, material wealth and the like are continuously increased, so that the economic loss of a rainstorm flood disaster is high.
Flood disaster risk assessment is a current hot research problem, and especially in the global warming background, how the flood risk will change in the future is the focus of attention. The global warming causes hydrologic cycle aggravation, flood influence factors are numerous, the difference of response rule and area is obvious, and the response result of the flood to the warming is not highly credible. In the aspect of multi-process and multi-mode coupling, global scale flood process simulation influence factors are various, physical processes are various, flood risk assessment relates to multiple aspects such as climate simulation, hydrological simulation and social economic simulation, and a cross-discipline multi-process and multi-model coupling flood simulation and assessment framework needs to be developed.
The development of numerical patterns provides a means for studying future climate change and the resulting extreme climate events. Especially, with the development of computers, the resolution of global climate modes is greatly improved, and global climate mode results of more than 20 climate mode groups and more than 50 sets participating in a fifth climate mode comparison plan (CMIP5) for an IPCC fifth evaluation report (AR5) provide a basis for researching future extreme climate event changes and disaster risks caused by the future extreme climate event changes. In the methods used in the past, the flood risk under the climate change background is mainly researched based on a global climate model or a single land hydrological model. The global climate mode output results have low resolution and no consideration is given to the convergence mechanism. A single land hydrological model cannot effectively assess the uncertainty of different production convergence mechanisms and simulation results. In addition, in the conventional research, the change of flood itself is mainly focused on flood risk assessment, and the influence of the development of social economy is ignored. Therefore, the global climate model, the land hydrological model and the socio-economic development path model are coupled, flood risks are comprehensively evaluated, accuracy and reliability of flood risk evaluation are improved, and achievement transformation from scientific technology to socio-economic direction is expected to be completed.
Disclosure of Invention
The invention aims to provide a method for evaluating future socio-economic development conditions based on a coupled climate model, a land hydrological model and a socio-economic model, aiming at the defects in the prior art.
The invention discloses a method for evaluating the influence of flood on social economy based on coupling model integrated simulation, which comprises the following steps:
step S1: collecting data; collecting actual measurement global meteorological data, hydrological data, land utilization data and topographic and geomorphic data;
step S2: designing different temperature rise scenes in the future; selecting the periods of 1 ℃, 1.5 ℃ and 2 ℃ of temperature rise respectively according to the special report of 1.5 ℃ of IPCC temperature rise; simulating global meteorological data under concentration paths (RCPs) of different greenhouse gas emissions by using a simulation result of a CMIP5 global climate mode on future climate change; taking the global climate modes as reference, generating global climate modes at different temperature rising periods according to different greenhouse gas emission concentrations;
step S3: establishing a land hydrological model; considering different flood discharging processes of a production convergence mechanism and an evaporation mechanism, coupling the global climate model generated in the step S2 to obtain a global climate model-land hydrological model coupling model, and performing model parameter calibration and model verification on the global climate model-land hydrological model coupling model according to the measured global meteorological data and hydrological data;
step S4: setting an initial field of a land hydrological model; carrying out down-scale analysis and bilinear interpolation on the simulated global meteorological data obtained in the step S2, and processing the simulated global meteorological data into global meteorological data consistent with the spatial scale of the land hydrological model;
step S5: land hydrological models simulate a flood process under different temperature rise scenes; driving the global climate model-terrestrial hydrological model coupling model which is subjected to model parameter calibration and model verification in the step S3 according to the terrestrial hydrological model initial field set in the step S4 and the processed global meteorological data which are consistent with the terrestrial hydrological model space scale, simulating a flood space-time change evolution process under the temperature rise condition, and storing the simulation result of the flood; repeating the process until the flood time-space change evolution process under all temperature rise scenes is completed;
step S6: establishing a social and economic impact evaluation model; establishing a global climate mode-land hydrological model-socioeconomic development path mode coupling mode according to different socioeconomic development paths;
step S7: evaluating the influence of flood on social economy in different future periods under different social economic development paths; calculating the social and economic exposure degree under different temperature rise scenes in different regions; and integrating and averaging land surface hydrological model results, comparing reference periods, and calculating the area, population and GDP exposure of flood variability and flood magnitude in different areas and different socioeconomic development paths.
In the method of the present invention, in step S1, the meteorological data includes the daily average air temperature, the precipitation, the evaporation capacity and the solar radiation of the global site; the hydrological data comprises daily precipitation, evaporation capacity and flood magnitude; land use data inverted by remote sensing satellites with a global resolution of 30m and digital elevation data with a global resolution of 30 m.
In step S2, the periods of 1 ℃, 1.5 ℃ and 2 ℃ temperature rise are identified by using a moving average calculation method, and concentration paths (RCPs) including 4 emissions of greenhouse gases, aerosols and the like are RCPs 2.6/4.5/6.0/8.5, respectively, according to a CMIP5 climate change estimation experiment. Each scenario includes a set of emissions and concentrations of greenhouse gases, aerosols and chemically active gases and a time course for land use or soil cover, on which basis a total of 12 future temperature rise scenarios are available.
Furthermore, according to the special report of IPCC temperature rise of 1.5 ℃, 1861-1900 years is taken as the global average surface temperature at the early stage of industrialization, and during 1971-2100 years, an average value is calculated every 30 years, the average value is higher than the temperature of the global average surface temperature at the early stage of industrialization, namely the temperature rise, and the periods of 1 ℃, 1.5 ℃ and 2 ℃ of temperature rise are selected.
Further, a running average calculation method is used, and the chronological average value of 30 years is calculated in sequence according to the time series data item by item to reflect the long-term trend. The numerical value of the time series is influenced by periodic variation and random fluctuation, the fluctuation is large, the development trend of the event is not easy to display, the influence of the factors can be eliminated by using a moving average method, and the development direction and the development trend of the event are displayed. The sliding average value calculation formula is as follows:
Ft＝(At+At-1+At-2+...+At-15+At+1+At+2+...+At+14)/n
in the formula, FtIs the predicted value of the moving average in the t-th year, n is the number of years of the moving average, AtActual value of year t, At-1Is the actual value of the previous year, At-2、At-3And At-nThe actual values of the last two years, the last three years and the last n years. Like At+2、At+3And At+nThe actual values of the last two years, the last three years and the last n years.
In the step S3, global climate mode variables comprise air temperature, air pressure, wind speed, humidity and the like, the land hydrological model considers the physical exchange process between atmosphere and vegetation and soil and reflects the state change of soil, vegetation and atmospheric hydrothermal and hydrothermal transmission, the land hydrological model needs three types of data information including vegetation parameters, soil parameters and atmospheric forcing data, the vegetation data and the soil data are respectively input according to an LDAS assimilation system and USDA data of the United states department of agriculture, in the atmospheric forcing data, the model is integrated step by step into day and simulates evaporation, flood and soil water, the input data needed by the model are daily precipitation, daily maximum air temperature and daily minimum air temperature, and the land hydrological model is compared with the global climate model and has the main characteristics that ① considers the hydrological balance and energy balance processes between land and air, ② considers a yield convergence mechanism, ③ considers the influence of soil nonuniformity on yield in the secondary grid, ④ considers the spatial nonuniformity in the secondary grid, and ⑤ considers the thawing and the soil thawing process.
Further, the basic equation set of the atmosphere according to the global climate mode is as follows:
CvdT+pdα＝dQ
p＝ρRT
in the formula (I), the compound is shown in the specification,
p is the air fluid density, P is the atmospheric pressure,
is the angular velocity of the earth's rotation,
in order to be the acceleration of the gravity,
is molecular viscous force, CvIs dry air ratio constant volume heat capacity, Q is heating rate of external source to air per unit mass, T is temperature,
is a gradient operator.
The land surface hydrological model is a large-scale distributed hydrological model, is represented by space distribution characteristics of soil with variable infiltration capacity, mainly considers physical exchange processes among atmospheric vegetation soil, reflects hydrothermal state changes and hydrothermal transmission among soil, vegetation and atmosphere, the land surface hydrological model comprises a snow and snow melting model based on temperature indexes, is used for simulating dynamic change characteristics of snow accumulation, considers snow accumulation, snow melting, soil freezing and thawing and other processes, and considers bare soil and various vegetation coverage types in each calculation grid, evaporation calculation of the model considers vegetation canopy interception, vegetation and bare soil evaporation forms, global ground coverage is divided into 14 types, 11 types of vegetation, and the rest 3 types of water, buildings and bare soil, water exchange, evaporation and flow generation among soil layers in each grid are determined by different vegetation types, and calculation is carried out by proportion calculation in upper and lower soil through Leaf Area Indexes (LAI), vegetation impedance and vegetation coverage indexes (through a) of each planting type, and vegetation coverage area calculation of the ground cover, calculation of the earth surface energy and water distribution coefficient, calculation of the earth surface area, the vegetation emission rate and the emission rate, and the emission rate:
water balance index (WB):
Qobs-Qsim
WB＝/Qobs
in the formula, QsimIs a simulated multi-year flood magnitude; qsimMany years flood magnitude for observation
Nash-Sutcliffe efficiency coefficient (R)2)：
In the formula (I), the compound is shown in the specification,
is the flood observed;
simulated flood. The following criteria were chosen: WB (wideband weight division multiple Access)<10％； R2>0.6。
In the step S4, according to the global data of the grid point analytic scale, the scale reduction analysis of the global climate mode adopts a scale reduction method, i.e., an SDSM model, in which multiple regression and a weather generator are coupled, to correct and scale-reduce the observation data and the mode simulation future data. The prediction of the atmospheric ocean circulation mode on the future climate is used as an input field, and smaller-scale climate information is generated. Since the land hydrological model usually needs information of the river basin scale, the method can establish a statistical relationship between a large-scale climate sequence and river basin climate elements by using years of observation data for verification. And then, the statistical relationship is checked by applying independent observation data, and finally, the relationship is applied to large-scale climate information output by a global climate mode so as to estimate future climate scenes of the drainage basin.
Further, the step of generating the future climate situation when the statistical downscaling method is applied is mainly divided into 5 steps, namely selecting ① large-scale climate forecast factors, selecting and calibrating ② statistical downscaling modes, verifying adaptability of the modes by using ③ independent observation data, applying the statistical modes to global climate mode results to generate the future climate situation by ④, and diagnosing, analyzing and carrying out feedback research on the future climate situation ⑤.
In step S4, the result of the global climate pattern simulation is interpolated using a bilinear interpolation method to drive the high-resolution land hydrological model, because the resolutions of the global climate patterns are different. The bilinear interpolation is the linear interpolation extension of an interpolation function with two variables, and the core idea is to perform linear interpolation in two directions respectively. The bilinear interpolation is calculated as follows:
if we want to get the value of the unknown function f at point P ═ x, y, we assume that we know the function f at Q11＝(x1,y1)、Q12＝(x1,y2)、Q21＝(x2,y1)、Q22＝(x2,y2) Values of four points. Firstly, linear interpolation is carried out in the x direction to obtain:
in the formula, R1＝(x,y1)、R2＝(x,y2)；
Then linear interpolation is carried out in the y direction to obtain:
the result is the final result of bilinear interpolation:
in the step S6, the social and economic development situation is reasonably set as the basis of the climate change, and is also a key link for evaluating the influence of the climate change. The socio-economic development model contains 5 shared socio-economic pathways (SSPs) altogether, reflecting the association between radiation compelling and socio-economic development. Each particular SSP represents a class of developmental patterns including a corresponding combination of developmental features and influential factors, such as population growth, economic development, technological advancement, environmental conditions, fairness principles, government regulation, globalization, and the like.
In the step S7, the land hydrological model results driven by the global climate mode are integrated and averaged, the integrated simulation average of the annual flood sequence is decomposed by a rotational experience orthogonal decomposition method, the corresponding rate signal-to-noise ratio of the flood magnitude to the air temperature is calculated, and the reliability of the integrated simulation is verified.
Further, the method of using integrated simulation can reduce the uncertainty of the simulation result. Aggregating simulation results can eliminate unpredictable portions of the pattern solution. The effect of ensemble averaging is due to the individual members, since the nonlinear filtering results in cancellation of the unpredictable part of the prediction, while the consistent part of the members is not eliminated in the averaging.
The ensemble average of weather fields is smoother than the individual members, because the long-term tendency to climate change is superimposed on the internal variability, so that the variation of the simulated variables depends on the phase and amplitude of the internal variability at the time of the start of the simulation, averaging the simulation results, and eliminating some of the effects of the internal variability. Furthermore, the evolution over time of a single prediction with ensemble averaging as the initial field at the initial instant differs from the time evolution of the ensemble prediction averaging in that the atmospheric mode is a highly non-linear function, transforming a set of initial values into a set of predictions, for the non-linear function f (x), with x representing the variable and n representing the ensemble membership:
the equation right represents applying a non-linear function (prediction mode) to the ensemble average, and the left is the ensemble prediction average. The method adopting the multi-mode set simulation is also the core of the IPCC climate change evaluation work.
In step S7, the land hydrological model and the socioeconomic development model are coupled, and socioeconomic exposure, i.e., the area exposed to flood, socioeconomic exposure, and the like, is calculated. And taking the flood magnitude and the flood variability of the reference period as the reference, counting the areas of the flood magnitude and the flood variability exceeding the reference period under different temperature rise scenes in the future, and superposing population and GDP on the exposed areas to obtain population and GDP exposure.
Further, the response of flood activity to global warming is mainly reflected in two aspects: flood volume level and flood variability. Selecting the maximum daily runoff of each year as a flood sequence, fitting the flood sequence by adopting Generalized Extreme Value (GEV) distribution, estimating the flood magnitude in 10, 20 and 50 years, wherein the distribution function of the generalized extreme value distribution is as follows:
in the formula, x is the value of a random variable, and a, k and u are distribution parameters. In the present study, based on the currently estimated flood strength thresholds for 10 years, 20 years and 50 years (i.e., 1971-.
Further, the standard deviations of the flood sequences were used to calculate the flood variability, based on the 1.25, 1.5 and 1.75-fold standard deviations of the 1971-2000 flood sequences as thresholds for the flood variability.
Aiming at the existing problem of evaluating the influence of flood risk on social economy, the invention establishes a global climate mode-land hydrological model coupling model by developing numerical experiments of flood process simulation under different production and confluence mechanisms and evaporation mechanisms on the basis of simulated climate conditions under different temperature rise scenes in the future of climate change prediction by taking a land hydrological model as a basis, and simulates the global flood process. And dynamically determining future socio-economic development conditions according to different socio-economic development paths, and evaluating the influence of flood on socio-economic development paths in different periods in the future. Compared with the traditional method, the method not only improves the space-time resolution of global flood process simulation and provides possibility for refined evaluation and management of global flood risks, but also fully considers the social economic development conditions of different regions under different social economic development paths while considering different production convergence mechanisms and evaporation mechanisms in the flood simulation process, so that the flood risks evaluated in the different regions and different time periods have more scientific and reasonable influence on social economy.
The method of the invention has the following beneficial effects:
(1) the method considers different production and confluence mechanisms and evaporation mechanisms, is suitable for flood changes of different time scales and watershed or grid scales of different climatic regions, simultaneously reduces errors as much as possible by using an integrated simulation method, changes the traditional method adopting single-mode simulation, and has wider application range and better effect.
(2) The socioeconomic development path mode is coupled, flood risks are comprehensively evaluated, accuracy and reliability of flood risk evaluation are improved, and references are expected to be provided for formulating disaster prevention and reduction policies and measures and disaster risk management.
(3) The temporal resolution and the spatial resolution are significantly improved. The time resolution of the current global climate system is basically in months, and the spatial resolution is mostly between 1 ° and 2 °. The method is based on the land hydrological model, the spatial resolution is obviously improved, the time resolution can reach the time scale of day, and support is provided for evaluation of flood risks to social economy.
Drawings
FIG. 1 is a flow chart of a coupling mode estimation method;
FIG. 2 is a graph of the change in earth surface temperature for different greenhouse gas emission concentration paths for global climate patterns;
FIG. 3 is a graph showing the response rate of the flood magnitude to air temperature and the signal-to-noise ratio of the integrated simulation results;
FIG. 4 is a result of REOF decomposition performed on an integrated simulation result of the flood sequence in 1971-2100 years;
FIG. 5 is the response of flood magnitude area exposure to temperature rise;
FIG. 6 is a response of population exposure to temperature increase based on flood level of 2000; .
FIG. 7 is the response of GDP exposure to temperature increase based on flood level of 2000;
FIG. 8 is the response of area exposure to flood variability to temperature increase;
figure 9 is the response of population exposure to temperature increase based on the flood variability of 2100 years for the SSP1 pathway.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the accompanying drawings, but the present invention is not limited to these embodiments.
The embodiment takes 1971-2100 in the world as an example, and further describes a technical scheme for evaluating the socioeconomic impact of flood based on a coupled global climate model-land hydrology model-socioeconomic development path model. The examples are intended to illustrate the invention, but are not intended to limit the scope of the invention, as the invention is equally applicable to different regions or time periods.
The implementation flow of the method for evaluating the socioeconomic impact of flood based on the coupled global climate mode-land hydrological model-socioeconomic development path mode is shown in figure 1, and comprises the following specific steps:
(1) collecting basic data: first, in this embodiment, the daily observation data of the river flow of 52199 sites distributed over the whole globe are collected from different countries, the time resolution is the daily observation data, and the time series is 1961-.
Table 1 shows the selected five CMIP5 patterns;
table 2 shows the eight selected land hydrological models;
(2) designing different temperature rise scenes in the future;
in this example, the time periods of 1.5 ℃ and 2 ℃ rise were analyzed. And (3) calculating the time-sequence average value of 30 years in sequence according to the time-sequence data item by using a moving average calculation method so as to reflect the long-term trend.
The moving average calculation formula is as follows:
Ft＝(At+At-1+At-2+...+At-15+At+1+At+2+...+At+14)/n
in the formula, FtIs the predicted value of the moving average in the t-th year, n is the number of years of the moving average, AtActual value of year t, At-1Is the actual value of the previous year, At-2、At-3And At-nThe actual values of the last two years, the last three years and the last n years. Like At+2、At+3And At+nThe actual values of the last two years, the last three years and the last n years. The results of the simulation were taken into the formula to calculate the running average every thirty years and thus the time periods of 1.5 and 2 c temperature rise were obtained. The highest greenhouse gas emission scenario used: RCP8.5, this scenario assumes the highest population, low technological innovation rate, slow energy improvement, slow income growth, resulting in long-term high energy demand and high temperature chamber gas emissions, and lack of policy to cope with climate change. Thus, results were generated for both data with a temperature rise of 1.5 or 2 ℃ under the RCP8.5 scenario. As shown in fig. 2, according to the specific report of 1.5 degrees of IPCC temperature rise, the global average temperature in the historical period of global climate pattern simulation and 4 temperature rise scenarios is calculated relative to the temperature rise in the early stage of industrialization. The 4 temperature rise scenarios are RCP2.6, RCP4.5, RCP6.0 and RCP8.5, respectively-the two vertical dashed lines represent the times reached under the RCP8.5 temperature rise scenario at 1.5 ℃ and 2 ℃ temperature rise, respectively.
(3) Establishing a land hydrological model;
① global climate pattern;
the basic equation set of the atmosphere according to the global climate mode is as follows:
CvdT+pdα＝dQ
p＝ρRT
in the formula (I), the compound is shown in the specification,
p is the air fluid density, P is the atmospheric pressure,
is the angular velocity of the earth's rotation,
in order to be the acceleration of the gravity,
is molecular viscous force, CvIs dry air ratio constant volume heat capacity, Q is heating rate of external source to air per unit mass, T is temperature,
is a gradient operator.
②, using 2010 world 30m land utilization data as underlying surface data input by a land hydrological model, using reduced scale meteorological data output by a global climate mode of a research area as forcing data, driving the land hydrological model to simulate the flood quantity of each grid of the research area, summarizing the flood quantity on each grid to a river network through a confluence model, simulating the flow of each main river of the research area, comparing the flow with the flow observed by a hydrological station, and calculating the optimal solution of a target function by adopting the MOCOM-UA optimization method so as to find 10 sets of parameters with the best simulation effect.
Before the numerical simulation experiment, parameter calibration is carried out according to measured flow data, wherein the parameters are a downward osmosis curve parameter B and a three-layer soil depth (D)1、D2And D3) And ARNO-based flow model parameters (D)S、DMAnd WS). Model correction and verification are carried out by adopting two digital indexes of a water balance index and a Nash-Sutcliffe efficiency coefficient:
water balance index (WB):
in the formula, QsimIs the simulated flood volume; qsimIs the flood volume observed;
Nash-Sutcliffe efficiency coefficient (R)2)：
In the formula (I), the compound is shown in the specification,
is the flood observed;
simulated flood. The following criteria were chosen: WB (wideband weight division multiple Access)<10％； R2>0.6。
(4) Setting a model initial field;
① correcting for global climate pattern bias;
and (3) performing deviation correction on the simulation data of the global climate mode by adopting observed data such as WATCH, GPCP and the like, and adjusting the probability distribution of extreme values to be consistent with the probability distribution of the extreme values of the observed data.
② downscaling analysis;
adopting a multiple regression and skynergy generator coupled downscaling method: the SDSM model. The large scale predictor is used as a parameter for the local day generator to condition precipitation and to reflect random variations in precipitation size on wet days. The method can be generally expressed as:
in the formula, wtIs the conditional probability of whether precipitation occurred on day t;
is the normalized jth predictor; a isjIs a regression coefficient estimated by a linear least squares method; w is at-1And at-1The precipitation occurrence probability of one day of lag (lag-1) and the corresponding regression coefficient are respectively considered, and the term is an optional term. Using a uniformly distributed random number Rt(rt is more than or equal to 0 and less than or equal to 1) to determine whether precipitation occurs. For a given time and place, if wt≤RtThen the day rains. On wet days, the amount of precipitation is reflected by Z-score:
in the formula, ZtIs the Z-score of day t, bjIs an estimated regression coefficient; bt-1And Zt-1Is corresponding to at-1And wt-1And Z-score, e of the previous day is a random error term that satisfies a normal distribution.
③ bilinear interpolation;
the known function f is in Q11＝(x1,y1)、Q12＝(x1,y2)、Q21＝(x2,y1)、Q22＝(x2,y2) Values of four points. Firstly, linear interpolation is carried out in the X direction to obtain:
in the formula, R1＝(x,y1)、R2＝(x,y2)；
Then linear interpolation is carried out in the y direction to obtain:
the result is the final result of bilinear interpolation:
and after the global climate model is interpolated to reach the resolution ratio which is the same as that of the land hydrological model, the global climate model is used as the atmospheric compulsive data of the land hydrological model, and the vegetation parameters and the soil parameters refer to the data of the LDAS assimilation system and the USDA data of the United states department of agriculture, so that the operation of 8 land hydrological models is driven to obtain flood data.
(5) Performing numerical simulation tests on the flood process under different temperature rise conditions;
driving 8 land hydrological models by using meteorological data of 5 global climate models under the RCP8.5 scene, carrying out numerical simulation of spatial and temporal evolution of a flood process, and storing a calculation result; and repeating the process until all the land hydrological model flood process simulation is completed.
(6) Establishing a social and economic impact evaluation model;
according to the 5 socioeconomic development paths, a global climate model-land hydrology model-socioeconomic development path model coupling mode is established, and the influence of flood on socioeconomic development paths is evaluated.
(7) Calculating the socioeconomic exposure degree under different temperature rise conditions in a different mode;
and respectively driving 8 different land hydrological models by using 5 global climate modes to obtain 40 simulation results in total, and calculating an integrated simulation average value. And decomposing the flood sequence by using a rotational empirical orthogonal decomposition method, wherein the leading spatial mode and the time mode are human activity warming influence signals in the flood sequence. The signal-to-noise ratio, i.e., the ratio of the average of the response rates of the integrated simulations to their standard deviations, is then calculated. A higher signal-to-noise ratio indicates a better simulation result. As shown in fig. 3, the response signal-to-noise ratio of the flood levels in east asia, south asia and europe to temperature is greater than 1, which shows that the coupling model simulation results have better reliability. Specifically, for every 1 ℃ increase in temperature, the east and south asian flood levels increased by 5.9% and 7.9%, respectively, while europe decreased by 5.9%.
And decomposing the flood sequence by using a rotational empirical orthogonal decomposition method, wherein the dominant spatial mode and the time mode are signals influencing the warming of human activities in the flood sequence. As shown in fig. 4, the uncertainty of the coupling model simulation is reduced by the integrated simulation of 40 global climate model-land hydrological model coupling models, and the flood sequence of the integrated simulation is decomposed into a temporal mode and a spatial mode by a rotational empirical orthogonal method to identify human activity signals in the flood sequence. Under the influence of human activity warming, the east and south asian flood risks respond positively to global warming, and regions typified by europe and north america respond negatively.
And calculating an integrated simulation result of the flood magnitude and the flood variability. The flood magnitude is calculated as follows:
selecting the maximum daily runoff of each year as a flood sequence, adopting Generalized Extreme Value (GEV) distribution fitting flood sequences, estimating the flood magnitude in 10, 20 and 50 years, wherein the distribution function of the generalized extreme value distribution is as follows:
in the formula, x is the value of a random variable, and a, k and u are distribution parameters. Based on the currently estimated threshold values of flood intensity considered as the flood intensity in the first 10 years, 20 years and 50 years (namely 1971-.
And counting the area of the grid points with the intensity of the flood magnitude exceeding the corresponding flood intensity in the reference period in the next 10 years, 20 years and 50 years, namely the area exposure of the flood magnitude. As shown in fig. 5, the temperature is increased by 1 ℃ per liter, the global medium and small magnitude flood area exposure is reduced by 1.7%, and the global extreme flood area exposure is increased by 1.9%. East and south asia are major areas where there is an increasing trend towards socio-economic exposure in global flood levels.
And (4) overlaying 2000-year population data on the exposed area, and counting the number of exposed population, namely the population exposure degree of flood water level. As shown in fig. 6, an increase in the east and south asian population ratios from 34.5% to 46.8% resulted in an increase in the global population exposure from 2.8%/c to 4.4% ° c.
And (4) overlaying 2000-year GDP data on the exposed area, and counting the total amount of the exposed GDP, namely the GDP exposure degree of the flood magnitude. As shown in fig. 7, an increase in the east and south asian GDP occupancy from 9.8% to 18.5% resulted in a shift from a 2.5% decrease in GDP exposure to a 1.7% increase in GDP exposure in the study area. If the temperature is raised by 0.5 ℃ less, the population proportion which can be avoided from being influenced by extreme flood is far higher than that of medium and small flood; in particular, extreme floods in east and south asia can avoid the impact with an area, population and GDP ratio 2-3 times that of medium and small floods.
The standard deviations of the flood sequences were used to calculate the flood variability, based on the 1.25, 1.5 and 1.75-fold standard deviations of the 1971-2000 flood sequences as thresholds for the flood variability.
And counting the grid point areas of which the future flood variability exceeds the standard deviation of the flood sequence in the reference period by 1.25, 1.5 and 1.75 times, namely the area exposure of the flood variability. As shown in FIG. 8, the total area exposure was 29.7%/34.2% at 1.5/2 ℃ temperature rise.
Overlaying the population of the SSP1 path based on 2100 years on the area exposed by the flood variability, and counting the number of the exposed population, namely the future population exposure degree of the flood variability; as shown in fig. 9, rapid economic development in east and south asia significantly accelerated the exposure of GDP to global flood rates, i.e., from 1.6%/c to 7.2%/c. The smaller the temperature rise by 0.5 degrees, the larger the flood rate, the higher the proportion of the global avoidable exposure, specifically, the area exposure was 27.1%, the population was 34.7%, and the GDP was 12%. The above is not mentioned, and is applicable to the prior art.
While certain specific embodiments of the present invention have been described in detail by way of illustration, it will be understood by those skilled in the art that the foregoing is illustrative only and is not limiting of the scope of the invention, as various modifications or additions may be made to the specific embodiments described and substituted in a similar manner by those skilled in the art without departing from the scope of the invention as defined in the appending claims. It should be understood by those skilled in the art that any modifications, equivalents, improvements and the like made to the above embodiments in accordance with the technical spirit of the present invention are included in the scope of the present invention.
Claims (10)
1. The method for evaluating the influence of flood on social economy based on coupling model integrated simulation is characterized by comprising the following steps of:
step S1: collecting data; collecting actual measurement global meteorological data, hydrological data, land utilization data and topographic and geomorphic data;
step S2: designing different temperature rise scenes in the future; selecting periods of 1 deg.C, 1.5 deg.C and 2 deg.C respectively according to IPCC report of temperature rise of 1.5 deg.C; obtaining simulation global meteorological data under concentration paths (RCPs) of different greenhouse gas emissions by utilizing a simulation result of the CMIP5 global climate mode on future climate change; taking the global climate modes as reference, generating global climate modes at different temperature rising periods according to different greenhouse gas emission concentrations;
step S3: establishing a land hydrological model; coupling the global climate model generated in the step S2 in consideration of flood processes under different production convergence mechanisms and evaporation mechanisms to obtain a global climate model-land hydrological model coupling model, and performing model parameter calibration and model verification on the global climate model-land hydrological model coupling model according to the measured global meteorological data and hydrological data;
step S4: setting an initial field of a land hydrological model; carrying out down-scale analysis and bilinear interpolation on the simulated global meteorological data obtained in the step S2, and processing the simulated global meteorological data into global meteorological data consistent with the spatial scale of the land hydrological model;
step S5: land hydrological models simulate a flood process under different temperature rise scenes; driving the global climate model-terrestrial hydrological model coupling model which is subjected to model parameter calibration and model verification in the step S3 according to the terrestrial hydrological model initial field set in the step S4 and the processed global meteorological data which are consistent with the terrestrial hydrological model space scale, simulating a flood space-time change evolution process under the temperature rise condition, and storing the simulation result of the flood; repeating the process until the flood time-space change evolution process under all temperature rise scenes is completed;
step S6: establishing a social and economic impact evaluation model; establishing a global climate mode-land hydrological model-socioeconomic development path mode coupling mode according to different socioeconomic development paths;
step S7: evaluating the influence of flood on socio-economy in different periods in the future under different socio-economy development paths; calculating the social and economic exposure degree under different temperature rise scenes in different regions; and integrating and averaging the land hydrological model results, comparing reference periods, and calculating the areas, population and GDP exposure of flood variability and flood magnitude in different areas and different socioeconomic development paths.
2. The method for assessing socioeconomic impact of flood water based on coupled model integrated simulation of claim 1, wherein in step S1, the measured global meteorological data includes daily average air temperature, precipitation, evaporation and solar radiation of each global site; the hydrological data comprise daily average precipitation, evaporation capacity and runoff capacity; the topographic data includes topographic height data for each region.
3. The flood evaluation method for socioeconomic impact on socioeconomic impact based on coupled model integrated simulation of claim 1, wherein in step S2, the periods of 1 ℃, 1.5 ℃ and 2 ℃ temperature rise are identified using a moving average calculation method, and the concentration paths of 4 greenhouse gas emissions are RCP 2.6/4.5/6.0/8.5 according to CMIP5 climate change estimation experiment; with this as a reference, twelve future temperature rise scenarios can be obtained.
4. The flood socioeconomic impact assessment method according to claim 2, wherein the global climate pattern variables include air temperature, air pressure, wind speed, and fluid density in step S3; the parameters of the land hydrological model comprise vegetation parameters, soil parameters and atmospheric compulsive data.
5. The flood socioeconomic impact assessment method based on coupled model integrated simulation of claim 1, wherein in step S2, the basic equation set of the global climate model atmospheric motion is:
CvdT＝pdα＝dQ
p＝ρRT
in the formula:
p is the air fluid density, P is the atmospheric pressure,
is the angular velocity of the earth's rotation,
in order to be the acceleration of the gravity,
is molecular viscous force, CvIs dry air ratio constant volume heat capacity, Q is heating rate of external source to air per unit mass, T is temperature,
is a gradient operator.
6. The method of claim 1, wherein in step S4, the global weather mode downscaling analysis adopts a multiple regression and a coupled down-scaling method of a weather generator, i.e. an SDSM model, to correct and downscale the global weather data simulated in the global weather mode in step S2 according to the measured global weather data and the hydrological data in step S1, and the bilinear interpolation method is used to interpolate the global weather data simulated in the global weather mode and drive the high-resolution land hydrological model.
7. The method of claim 1, wherein in step S7, the flood simulated by the global climate model-land hydrological model in step S5 is subjected to ensemble averaging, the average value of the flood ensemble simulation is decomposed by a rotational empirical orthogonal decomposition method, and the corresponding rate signal-to-noise ratio of the flood magnitude to the air temperature is calculated to verify the reliability of the ensemble simulation.
8. The method of claim 1, wherein in step S7, socioeconomic exposure is calculated, and areas where the flood magnitude and the variability under different future temperature rise scenarios exceed the reference period are counted, i.e., area exposure, and population and GDP on the exposure area are superimposed, i.e., population and GDP exposure, based on the flood magnitude and variability of the reference period.
9. The flood socioeconomic impact assessment method based on the coupled model integrated simulation of claim 1, wherein the flood sequence is fitted with the generalized extremum distribution in step S7, and the flood level is estimated at 10, 20, and 50 years, and the distribution function of the generalized extremum distribution is as follows:
in the formula: x is the value of the random variable, and a, k and u are distribution parameters.
10. The method for evaluating socioeconomic impact of flood based on coupling model integrated simulation of claim 1, wherein the standard deviations of the flood sequences are used to calculate the flood variability in step S7, and the standard deviations of the flood sequences are used as thresholds of the flood variability according to 1971-2000 times.
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| US20230161072A1 (en) * | 2021-11-23 | 2023-05-25 | At&T Intellectual Property I, L.P. | Predictive Hydrological Impact Diagnostic System |
| CN115034548A (en) * | 2022-04-20 | 2022-09-09 | 中国地质大学（武汉） | Flood seasonal driving mechanism identification and attribution method based on partial differential equation |
| CN115034548B (en) * | 2022-04-20 | 2024-05-17 | 中国地质大学（武汉） | Partial differential equation-based flood seasonal driving mechanism identification and attribution method |
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