A Method for Identifying Warming Signals of Arid Area Expansion and Assessing Population Impact

Abstract

The present invention discloses a method for identifying warming signals of arid area expansion and assessing population impacts. The present invention uses observational data, reanalysis data, and data from historical experiments and future simulation experiments with different socioeconomic development pathways in CMIP6 to analyze the changes in global dry-wet variations, arid areas, and populations in arid regions, and uses correlation analysis methods and optimal fingerprint methods for analysis. Explore the global dry-wet spatio-temporal variation patterns and their driving factors; detect and attribute the changes in global dryland area, and identify the external forcings affecting the changes in dryland area; analyze the changes in the population in arid regions and their influencing factors. And quantitatively evaluate the contribution rate of anthropogenic climate change in these changes. The present invention explores the laws of the evolution of drylands and the population in drylands from the perspective of meteorological drought, and is applicable to global and arid region research. It helps to clarify the impact of climate change affected by human activities on drought, and helps drylands to cope with future global climate change.

Description

Technical Field

[0001] The present invention belongs to the field of atmospheric science, and particularly relates to a method for identifying warming signals of arid area expansion and evaluating population impacts. Background Art

[0002] The evolution of drylands is a current hot research topic. So far, extensive research has been conducted on the drought intensity and the extent of drylands. Currently, it is generally obtained that atmospheric drought shows a trend of becoming drier in the future. There are also drought analyses from the perspectives of terrestrial water storage, soil moisture, and precipitation, obtaining the trend of future drought. However, there are also those based on ecological hydrological indices that suggest a trend of wetting in the future. The existing research has not clearly determined whether the spatio-temporal changes of drylands are affected by climate change caused by human activities. The external forcings affecting dryland changes have not been clarified. Therefore, this method provides a quantitative method to quantify the role of external forcings, in order to gain a deeper understanding of the patterns and causes of dryland changes. Populations at the global and regional scales are at risk of drought. Summary of the Invention

[0003] In order to solve the problems existing in the prior art, this method analyzes the changes and influencing factors of the population in arid areas from the perspective of the changes in the population in arid areas, in order to help understand the potential impacts of dryland changes on the population.

[0004] The present invention provides a method for identifying warming signals of arid area expansion and evaluating population impacts, and the method includes the following steps:

[0005] Step S1: Data collection; collect observed or reanalyzed precipitation and potential evapotranspiration data at the global or regional scale, or variables capable of calculating potential evapotranspiration; collect actual and predicted population data; collect precipitation, latent heat flux, sensible heat flux, relative humidity, wind speed, maximum temperature, minimum temperature, and elevation data of the sixth phase of the Coupled Model Intercomparison Project CMIP6;

[0006] Step S2: Calculate potential evapotranspiration and drought index; use the Penman-Monteith formula recommended by the Food and Agriculture Organization of the United Nations, combine the data obtained in Step S1, and calculate potential evapotranspiration; combine precipitation and use the ratio of precipitation to potential evapotranspiration to calculate the drought index used to characterize atmospheric drought;

[0007] Step S3: Spatial patterns of wet-dry changes and their driving factors; combine the precipitation data obtained in Step S1 and the potential evapotranspiration and drought index calculated in Step S2, and calculate the changes in these three variables at the spatial grid points in recent years relative to history, reflecting the spatial change pattern of drought;

[0008] Compare the correlation coefficient between pre - industrial revolution experiments and average observations with the Spearman correlation coefficients between historical climate simulation experiments, greenhouse gas forcing experiments, natural forcing experiments and average observations, and analyze the driving factors of drought change in combination with the spatial variations of dry and wet conditions;

[0009] Step S4: Temporal patterns of dry - wet changes and their driving factors; Combining the drought index obtained in Step S2, obtain the time - series of anomaly values of the drought index for historical and future scenarios, compare the CMIP6 experiments with the average of observations, and analyze the temporal change patterns and their driving factors;

[0010] Step S5: Temporal change pattern of dry - land area; Define the area with a drought index less than a as the drought area. Combining the drought index obtained in Step S2, identify the grid points with a drought index less than a to obtain the annual total drought - area time - series, and calculate the anomaly values using the reference period to finally obtain the anomaly time - series of the drought area; where a is a preset value, 0 < a < 1; and the reference period refers to a preset period of time;

[0011] Compare and analyze the trend change values of observations and each CMIP6 experiment, and analyze the temporal change pattern of the drought area in combination with the results obtained in Step S4;

[0012] Step S6: Detection and attribution of the temporal change of dry - land area; Combine the correlation coefficient of the five - year average time - series of the pre - industrial revolution drought area and the observed drought area, and compare the correlation coefficients of historical climate model experiments, greenhouse gas forcing experiments, and natural forcing experiments with the observed values to analyze which external - forcing - simulated experimental change is more consistent with the observed change of the dry - land area, and analyze the influencing factors of the temporal change of the dry - land area;

[0013] Use the optimal fingerprint method of univariate and bivariate to detect and attribute external forcing factors.

[0014] Step S7: Population change pattern in drought - affected areas; Combine the temperature data obtained in Step S1 and use the reference period to calculate the temperature increase level in the prediction period; where the prediction period also refers to a preset period of time, but the prediction period is after the reference period;

[0015] Combine the drought index calculated in Step S2 and the population data obtained in Step S1, and correspond the changes in the drought area and the changes in the population in the drought - affected areas with the temperature increase level to obtain the 20 - year moving average series of the drought area and population under different temperature increase levels, and analyze the relationship between the change in the drought area and the change in the population, and the relationship between the population, the drought area and the temperature increase level;

[0016] Step S8: Attribution of population change in drought - affected areas; Divide the population change in drought - affected areas into the population affected by population density and the population affected by both the change in drought area and population density;

[0017] Combine the drought index obtained in step S2, and the population data and temperature data obtained in step S1 to obtain the 20-year moving average time series of the population under different warming levels for the prediction period of the two parts;

[0018] Compare and analyze the population changes in the two parts, and the population change patterns under different warming levels in each different future scenario; analyze the main factors affecting the population changes in the arid area, and analyze the contribution rate of the combined effects of the changes in population density and the changes in arid area and population density.

[0019] Furthermore, in step S1, the observed or reanalyzed data are multiple sets of different precipitation and potential evapotranspiration data, and the average value of each set of data and multiple sets of data is analyzed.

[0020] Furthermore, in step S2, the Penman-Monteith formula is used to calculate the potential evapotranspiration, and the calculation formula is as follows:

[0021]

[0022] In the formula, PET represents potential evapotranspiration, R n represents the net surface radiation, G represents the soil heat flux density, T represents the daily average temperature at 2 meters high, u2 represents the wind speed at 2 meters high, e s represents the saturation water vapor pressure, e a represents the actual water vapor pressure, Δ represents the slope of the vapor pressure curve, and γ represents the psychrometric constant; where e s is calculated from the maximum temperature and the minimum temperature, and the formula is as follows:

[0023]

[0024] e a is calculated from the relative humidity and the saturation water vapor pressure, and the formula is as follows:

[0025]

[0026] R n is obtained by the following formula:

[0027] R n =R ns -R nl

[0028] where R ns is the incoming net shortwave radiation, and R nl is the outgoing net longwave radiation;

[0029] R n -G is calculated by the following formula:

[0030] R n-G = LH + SH

[0031] where LH is the latent heat flux and SH is the sensible heat flux;

[0032] Based on the calculated potential evapotranspiration, the atmospheric drought is characterized by the drought index, and the formula is as follows:

[0033] AI = P / PET

[0034] where P represents precipitation and PET represents potential evapotranspiration. When AI is less than b, it indicates that the area is a drought area; b is a preset threshold.

[0035] Furthermore, step S3 is specifically as follows:

[0036] S31: Divide into two different periods, the recent period and the historical period. Use the multi-year average of the drought index, precipitation, and potential evapotranspiration in the recent years to subtract the multi-year average in the historical period to reflect the changes in the drought index, precipitation, and potential evapotranspiration in the two different periods. The specific formula is as follows:

[0037] ΔAI = AI pres - AI past

[0038] where AI pres represents the multi-year average of drought for each grid point in the recent years, and AI past represents the multi-year average of drought for each grid point in the historical period;

[0039] S32: Perform multi-set or multi-model averaging on the observed and reanalysis data, historical climate simulation experiments, greenhouse gas forcing experiments, and natural forcing experiments to obtain the spatial distributions in different periods and different scenarios. Then subtract the historical period from the recent period to finally obtain the changing spatial distribution;

[0040] S33: Compare the changing directions and amounts of potential evapotranspiration, precipitation, and drought index to analyze the roles of precipitation and potential evapotranspiration in influencing the drought index;

[0041] Based on the dry-wet space, calculate the Spearman correlation coefficient between the spatial changes of observations and model simulations, compare the positive and negative changes and the magnitudes of the correlation coefficients of the observed and reanalysis data with the historical climate simulation experiments, greenhouse gas forcing experiments, and natural forcing experiments, analyze the main external forcings affecting the observed data, and obtain the driving factors of the dry-wet change spatial pattern.

[0042] Furthermore, the formula of the optimal fingerprint method in step S6 is as follows:

[0043] y = (X - α)β + ∈

[0044] Where y represents the time series of dryland observations, X represents the simulated time series of dryland area, including historical climate experiments, greenhouse gas forcing, and natural forcing; β represents the scaling factor; ∈ represents the regression residual;

[0045] Quantifying the attribution of observed changes in drylands to external forcing is:

[0046] Con = Slope × β

[0047] Where slope represents the linear trend of simulated drylands under each external forcing.

[0048] The beneficial effects of the present invention are:

[0049] (1) The present invention makes up for the lack of quantitative detection and attribution research on dryland area in previous studies, and quantifies the effects of different external forcings on the change of dryland area. The present invention uses multiple sets of observational and reanalysis data, reducing the uncertainty in the research process. At the same time, through the method based on correlation analysis and the optimal fingerprint method, analysis is carried out on the basis of the two methods to detect the signals of external forcings and quantify the attribution, making the results more credible. And the present invention is applicable to the global or individual regions.

[0050] (2) By distinguishing the changes in dryland population, the present invention differentiates the changes in dryland population caused by the change in population density and the changes in population caused by the change in drylands, which helps to better understand the changes in dryland population and provides theoretical support for decision-makers to adapt to and mitigate the impact of climate change on population. Brief Description of the Drawings

[0051] Figure 1 is a schematic flow chart of a method for identifying warming signals and assessing population impacts of dryland area expansion provided by the present invention;

[0052] Figure 2 Correlation-based attribution analysis of spatial pattern changes in drought index, precipitation, and potential evapotranspiration;

[0053] Figure 3 Time evolution of the proportion of global land area experiencing drying;

[0054] Figure 4 Long-term changes in the area of dryland regions in observations and model simulations;

[0055] Figure 5 Correlation-based attribution analysis of long-term temporal changes in dryland extent based on observational data and simulated multi-model data;

[0056] Figure 6 Detection and attribution of long-term changes in drylands based on the optimal fingerprint method;

[0057] Figure 7 Projected changes in the area of drylands and population during future warming periods relative to the 1961 - 1990 reference period under four future emission scenarios;

[0058] Figure 8 Projections of dryland population changes caused by changes in population density and the spatial extent of drylands during future warming periods relative to the 1961 - 1990 reference period under four future emission scenarios. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] The present invention provides a method for identifying warming signals of dryland area expansion and assessing population impacts. Please refer to Figure 1 , Figure 1 which is a flowchart of the method of the present invention; the method includes the following steps:

[0061] Step S1: Data collection; collect observed or re - analyzed precipitation and potential evapotranspiration data at the global or regional level, or variables capable of calculating potential evapotranspiration (such as downward short - wave radiation, net short - wave radiation, net long - wave radiation, specific humidity, maximum temperature, minimum temperature, wind speed, surface elevation, etc. data); collect precipitation, latent heat flux, sensible heat flux, relative humidity, wind speed, maximum temperature, minimum temperature and elevation data of the sixth phase of the Coupled Model Intercomparison Project CMIP6;

[0062] In the method of the present invention, in step S1, in order to reduce the uncertainty of detection and attribution, the observed or re - analyzed data are multiple sets of different precipitation and potential evapotranspiration data, and each set of data and the average value of multiple sets of data are analyzed. For the data set that does not contain potential evapotranspiration, the variables required to calculate potential evapotranspiration according to the Penman - Monteith formula are collected. For the CMIP6 data in step S1, the sensible heat flux, latent heat flux, wind speed, maximum temperature, minimum temperature, and relative humidity are selected, and in each experiment, the common models of these variables are selected. In order to analyze the dryland population, the present invention collects global population data in different historical periods and future projected population data, and downscales the population data, using the population data closest to the year of each different period.

[0063] Step S2: Calculate potential evapotranspiration and drought index; use the Penman - Monteith formula recommended by the Food and Agriculture Organization of the United Nations, combine the data obtained in step S1 to calculate potential evapotranspiration; combine precipitation and use the ratio of precipitation to potential evapotranspiration to calculate the drought index used to characterize atmospheric drought;

[0064] In step S2, the Penman-Monteith formula is used to calculate the potential evapotranspiration, and the calculation formula is as follows:

[0065]

[0066] In the formula, PET represents potential evapotranspiration, R n represents net surface radiation, G represents soil heat flux density, T represents the daily average temperature at 2 m height, u2 represents the wind speed at 2 m height, e s represents saturated water vapor pressure, e a represents actual water vapor pressure, Δ represents the slope vapor pressure curve, and γ represents the psychrometric constant; where e s is calculated from the maximum temperature and the minimum temperature, and the formula is as follows:

[0067]

[0068] e a is calculated from relative humidity and saturated water vapor pressure, and the formula is as follows:

[0069]

[0070] In the Penman-Monteith formula, if only R can be calculated n, then G is much smaller than R n and can often be ignored. R n is obtained from the following formula:

[0071] R n = R ns - R nl

[0072] where R ns is the incoming net shortwave radiation, and R nl is the outgoing net longwave radiation;

[0073] In CMIP6, there are two variables, sensible heat flux and latent heat flux, output. Therefore, R n - G is calculated from the following formula:

[0074] R n - G = LH + SH

[0075] where LH is the latent heat flux and SH is the sensible heat flux;

[0076] Based on the calculated potential evapotranspiration, the atmospheric drought is characterized by the drought index, and the formula is as follows:

[0077] AI = P / PET

[0078] Among them, P represents precipitation, and PET represents potential evapotranspiration. When AI is less than b, it indicates that the area is an arid region; b is a preset threshold, and in the present invention, b takes a value of 0.65;

[0079] Step S3: Spatial patterns of wet-dry changes and their driving factors; combining the precipitation data obtained in step S1 and the potential evapotranspiration and drought index calculated in step S2, calculate the changes in these three variables for the spatial grid points in recent years relative to the historical period, and reflect the spatial change pattern of drought;

[0080] Compare the correlation coefficient between the pre-industrial revolution experiment and the average observation with the Spearman correlation coefficients between the historical climate simulation experiment, the greenhouse gas forcing experiment, the natural forcing experiment and the average observation, and combine the spatial changes of wet and dry to analyze the driving factors of drought changes;

[0081] In the above step S3, two different periods are divided, the recent period and the historical period. Use the multi-year average values of the drought index, precipitation and potential evapotranspiration in recent years to subtract the multi-year average values in the historical period, so as to reflect the changes in the drought index, precipitation and potential evapotranspiration in the two different periods. Thus, the wet-dry changes globally or regionally are reflected. The calculation formula is as follows:

[0082] ΔAI = AI pres - AI past

[0083] Where AI pres represents the multi-year average value of drought for each grid point in recent years, and AI past represents the multi-year average value of drought for each grid point in the historical period.

[0084] Perform multi-set or multi-model averaging on the observed and reanalysis data, the historical climate simulation experiment, the greenhouse gas forcing experiment and the natural forcing experiment to obtain the spatial distributions in different periods and different scenarios, then subtract the historical period from the recent period, and finally obtain the spatial distribution of the changes.

[0085] Compare the change directions and change amounts of potential evapotranspiration, precipitation and drought index to clarify the roles of precipitation and potential evapotranspiration in affecting the drought index.

[0086] On the basis of clarifying the wet-dry space, calculate the Spearman correlation coefficient between the spatial changes of the observations and the spatial changes simulated by the model. The formula for the correlation coefficient is as follows:

[0087]

[0088] Compare the positive and negative changes and the magnitudes of the correlation coefficients between the observed and reanalysis data and the historical climate simulation experiment, the greenhouse gas forcing experiment and the natural forcing experiment, and analyze the main external forcings affecting the observed data. Thus, analyze the driving factors of the wet-dry change spatial pattern.

[0089] Step S4: Temporal patterns of wet-dry changes and their driving factors; Combining the drought index obtained in Step S2, for historical and future scenarios, obtain the time series of anomaly values of the drought index, compare the CMIP6 experiments with the observational mean, and analyze the temporal change patterns and their driving factors;

[0090] In the said Step S4, distinguish between historical and future periods, identify the areas where the globe is drying (areas with decreasing AI), and to reduce the uncertainty between data, calculate the ratio of the drying areas to the reference period to obtain the historical and future temporal change trends of drought.

[0091] Compare the observational results in the historical period with the results of historical experiments, greenhouse forcing results, and natural forcing results, analyze the main external forcing factors affecting the drying areas, clarify whether human activities play a role and in which direction. Combine the results obtained in Step S3 to obtain the spatio-temporal characteristics of global wet-dry changes.

[0092] Compare the changes in different future greenhouse gas emission scenarios to clarify the changes in the future drying trend under different scenarios.

[0093] Step S5: Temporal change pattern of dryland area; Define the areas with a drought index less than a as drought areas. Combining the drought index obtained in Step S2, identify the grid points with a drought index less than a to obtain the time series of the annual total drought area, and use the reference period to calculate the anomaly value, and finally obtain the anomaly time series of the drought area; where a is a preset value, depending on the drought index used; the reference period refers to a preset period of time; the drought index used in the present invention is the ratio of precipitation to potential evapotranspiration, so, a is taken as 0.65; the reference period of Step S5 is from 1961 to 1990;

[0094] Compare and analyze the trend change values of observations and various CMIP6 experiments, and divide the pre-industrial revolution experiments into several threads with the same length as the research period (65 years in this article). Using the same calculation method as the historical climate experiment, calculate the time series of the change trends of the drought areas in several pre-industrial revolution experiments and their several trend values. Finally, compare the trend values of the observational data, historical climate simulation experiments, greenhouse gas forcing experiments, and natural forcing experiments with the several trend values before the industrial revolution, and combine the results obtained in Step S4 to analyze the temporal change pattern of the drought area. Here, it is basically possible to identify whether greenhouse gas forcing, historical climate simulation forcing, or natural forcing can well simulate the observed change trend;

[0095] Step S6: Detection and attribution of the temporal change in dryland area; By combining the correlation coefficient of the pre-industrial drought area and the five-year average time series of the observed drought area, compare the correlation coefficients of the historical climate model experiments, greenhouse gas forcing experiments, and natural forcing experiments with the observed values, analyze which external forcing simulation experiment the observed change in dryland area is more consistent with, and analyze the influencing factors of the temporal change in dryland area;

[0096] Use the univariate and bivariate optimal fingerprint methods to detect and attribute external forcing factors. The formula of the optimal fingerprint method can be expressed as:

[0097] y = (X - α)β + ∈

[0098] Where y represents the observed time series of dryland, X represents the simulated time series of dryland area, including historical climate experiments, greenhouse gas forcing, and natural forcing. β represents the scaling factor. If the scaling factor is significantly greater than 0, it is considered that the external forcing factor can be detected. When the scaling factor is around 1, it means that the model response is consistent with the observed change. When the scaling factor is significantly greater than 1, it indicates that the model simulation result underestimates the observed change. Conversely, when the scaling factor is less than 1, it means that the model simulation result overestimates the observed change.

[0099] Both the univariate and bivariate optimal fingerprint methods use two methods, ordinary least squares and total least squares, for estimation. After obtaining the scaling factor, attributing the observed change in dryland to external forcing can be quantified as:

[0100] Con = Slope × β

[0101] Where Slope represents the linear trend of the simulated dryland under each external forcing.

[0102] Step S7: Population change pattern in the arid region; Combine the temperature data obtained in Step S1, and use the reference period to calculate the temperature increase level in the prediction period; The prediction period also refers to a preset period of time, but the prediction period is after the reference period; The reference period of Step S7 is from 1861 to 1900; The prediction period of Step S7 is from 1991 to 2100;

[0103] Combine the drought index calculated in Step S2 and the population data obtained in Step S1, correspond the change in drought area and the change in population in the arid region to the temperature increase level, obtain the 20-year moving average sequences of drought area and population under different warming levels, analyze the relationship between the change in drought area and the change in population, and the relationship between population, drought area, and warming level; According to the obtained population data and the calculated warming level, use the following formula to quantify the change in dryland population relative to the reference period:

[0104]

[0105] Among which, P w represents the average population during the future climate warming period, and P r represents the average population during the dryland reference period.

[0106] Step S8: Attribution of population change in arid regions; combining the drought index obtained in step S2, and the population data and temperature data obtained in step S1, to obtain the 20-year moving average time series of the population under different warming levels during the prediction period of the two parts. Divide the population change in arid regions into the population affected by population density and the population affected by the combined effect of drought area change and population density; therefore, the change in dryland population can be further decomposed into:

[0107] ΔP = (A + A i )P i - (A + A0)P0

[0108] Among which, A is the dryland shared by the warming period and the reference period, A0 is the dryland unique to the reference period, and A i is the dryland unique to the i-th year of the warming period. P0 is the population density during the reference period. P i is the population density in the i-th year.

[0109] For the change in the dryland population caused by the change in only population density and the change in dryland space and the change in population density caused by regional change, these two aspects of changes can be quantified by the following formula:

[0110]

[0111] Among which, ΔP pop represents the change from the change in population density, and ΔP area represents the change caused by the change in dryland space

[0112] Compare and analyze the population change patterns of the two parts of population changes under different future scenarios and different warming levels; analyze the main factors of population change in arid regions, and analyze the contribution rates of the change in population density and the combined effect of the change in drought area and population density.

[0113] As an example, the present invention takes the world from 1950 to 2014 as an example to further describe the present invention. The implementation case is used to illustrate the present invention, but this case is not used to limit the application scope of the present invention. It is also applicable to other regions and other time periods.

[0114] The specific steps of the method of the present invention are as follows:

[0115] (1) Collection of basic data;

[0116] In this example, observational and reanalysis data from four sources were collected, and the variables they provide are shown in Table 1.

[0117] Table 1 Observation datasets for calculating the drought index

[0118]

[0119] CMIP6 data was also collected, and the historical experiments are shown in Table 2 below.

[0120] Table 2 Models of the selected CMIP6 historical experiments

[0121]

[0122]

[0123] Note: "*" ("-") indicates that this experiment has (does not have) a model.

[0124] The CMIP6 future experiments are shown in Table 3.

[0125] Table 3 Models of the selected CMIP6 future experiments under different socio-economic development pathways

[0126]

[0127]

[0128] Note: "*" ("-") indicates that this experiment has (does not have) this model.

[0129] (2) Spatial variation patterns of dry-wet changes and their driving factors

[0130] According to the fact that after 1981, the main external forcing affecting the globe was greenhouse gases, while in the period of 1950 - 1975, the main external forcing affecting the globe was aerosols. In this example, the period of 1981 - 2014 was used to represent the recent years, and the period of 1950 - 1975 was used as the reference period for dry - wet changes. The multi - year grid average of 1981 - 2014 was subtracted from the grid average of 1950 - 1975, and a total of three variables, namely precipitation, potential evapotranspiration, and drought index, were used. 63% of the grid points on the global land surface (excluding Antarctica and Greenland) showed a downward trend, presenting a drying trend. Precipitation decreased in 48% of the land areas, while potential evapotranspiration increased in 83% of the land areas. By comparing with the observed data, the increase in potential evapotranspiration led to the drying trend. Then, by comparing the change trends of individual grid points, the drought index and precipitation showed a highly consistent positive - negative direction of change (in 83% of the land areas). Therefore, it can be considered that precipitation affected the spatial pattern of the drought index. By comparing the historical forcing, the consistency between greenhouse gas forcing and natural forcing with the observed data, it was analyzed that external forcing could better reflect the observed trend, and the external forcing affecting the spatial patterns of the drought index, precipitation, and potential evapotranspiration was analyzed. The simulated drought index by the model was poor, and the influence of external forcing on the spatial pattern of the drought index was not obvious.

[0131] Calculate the spatial correlation coefficient between the observations and the external forcing, and compare it with the spatial correlation coefficient between the pre - industrial - revolution forcing and the observations (as Figure 2 shown). If it exceeds the 90% confidence interval, it is considered that the role of this external forcing on the spatial variation is significant. There is a weak spatial correlation in the drought index, precipitation, and potential evapotranspiration of the historical climate experiments.

[0132] (3) Temporal patterns of dry - wet changes and their driving factors

[0133] Calculate the ratio of the drying area relative to the reference period, distinguish between historical and future periods, and obtain the time series of the area change of the drying area. In the historical time series, greenhouse gas forcing could well capture the observed change trend (being relatively consistent with the growth amount of the observed change), and historical climate forcing underestimated this trend, as shown in Figure 3 a. These can reflect that more and more land is experiencing atmospheric drying, and it may be caused by human - induced climate change. In the future time series (as shown in Figure 3 b), in different warming scenarios, except for the low - emission scenario, all show a significant growth trend.

[0134] (4) Temporal change pattern of the dry - land area

[0135] Identify the grid points where the drought index is less than 0.65, and obtain the time series of the anomaly value of the drought area relative to the reference - period climate state (as Figure 4as shown). By calculating its trend value, it can be found that the area of dry land has increased significantly during the observation, about 5.65% per century ( Figure 4 as shown in a of Figure 4 ). However, the historical climate experiment underestimated the observed changes. The simulation of greenhouse gases increased more significantly than other simulations (increasing by 4.47% per century, as shown in c of Figure 4 ), and was also closer to the observed changes. This indicates that greenhouse gases play an important role in the change of the dry land area. The trend values observed and those simulated by greenhouse gas forcing both exceed the range of the trends predicted by natural climate variability. And both are higher than 99% (the results of the greenhouse gas forcing experiment are relatively consistent with the observations, as shown in f of Figure 4 ). The historical climate experiment simulation (as shown in b of Figure 4 ) also obtained a trend of increasing dry land area, but underestimated the observed value, probably due to the effect of other forcings, such as aerosol forcing. The natural forcing (as shown in d of Figure 4 ) and the pre-industrial revolution forcing (as shown in e of

[0136] ) both show a slight decrease in dry land. These can reflect that within the range of dry land, the signal of greenhouse gas emissions is strong enough.

[0137] Taking the average of the dry land area every 5 years, calculating the correlation coefficient of the time change before the industrial revolution and the observation, and comparing the correlation coefficient of the time change trend between the external forcing and the observation to detect the external forcing affecting the dry land area (as shown in Figure 5 ). The Spearman correlation coefficients of the time series of the dry land area between the observation and the simulation of the historical climate scenario experiment are all relatively high, higher than the 85th percentile of the correlation coefficient between the observation and before the industrial revolution. The correlation between greenhouse gases and the observation is relatively high, exceeding 98% of the correlation coefficient between before the industrial revolution and the observation, indicating that greenhouse gas emissions have promoted the expansion of dry land. However, the correlation between the natural forcing experiment and the observation is weak, and within the coverage of the correlation coefficient between before the industrial revolution and the observation, indicating that the possibility of driving the observed changes by natural forcing alone is very low.

[0138] To further detect the signal and quantify the contribution of external forcing to the long-term time change of the dry land area, the optimal fingerprint method was used for analysis, and the results are as shown in Figure 6 . The code and specific operation process of the optimal fingerprint method here were provided by the team of Zhang Xuebin. The scaling factors estimated by the optimal fingerprint method are all greater than 1 for both the historical climate experiment and the greenhouse gas forcing experiment. It can be considered that their signals can be detected. However, at the same time, it also means that the results of the model simulation underestimated the amplitude of the observed dry land expansion. The scaling factors of the natural forcing experiment are not all significantly higher than 0. Therefore, the signal of natural forcing has not been detected (as shown in Figure 6as shown in a and d in). In this regard, further double-signal analysis (such as Figure 6 as shown in b and e in) was adopted, and it was found that the signal of greenhouse gas forcing could be separated from natural forcing, and the existence of its signal was robustly detected. Then, according to the formula for quantifying the contribution of external forcing in step S6, it can be obtained that the historical climate simulation experiment promoted the expansion of drylands by 2.36%, while the greenhouse gas forcing caused the expansion of drylands to be 4.63%, which is quite consistent with the change range of the observed values (such as Figure 6 as shown in c and f in).

[0139] (6) Patterns of population change in arid regions and their attribution

[0140] According to the formula in step S7, the changes in the area of drylands and the population in drylands from 1991 to the end of the 21st century were quantified. It can be found that in different emission scenarios, the area of drylands shows an increasing trend along with the temperature rise. And regardless of the high-emission scenario or the low-emission scenario, the trend of change in the area of drylands is relatively consistent (such as Figure 7 as shown in a and b in), so the change in the area of drylands is mainly affected by the temperature rise.

[0141] On the basis of analyzing the change in the area of drylands, by distinguishing the changes in the population under different future scenarios, it can be found that under the SSP370 scenario, the future population shows a continuous increasing trend, while in other scenarios, it shows a trend of increasing first and then decreasing, which may be related to the initial setting of the future scenarios. The population in drylands begins to show a decreasing trend mainly starting from a temperature rise of 3.5 °C (such as Figure 7 as shown in c and d in).

[0142] After clarifying the change in the population in drylands, according to the two aspects that lead to the change in the population in arid regions in step S7, the two aspects that affect the change in the population in drylands were quantified separately. The quantification methods for these two parts respectively study whether the change in population density and the spatial change in drylands dominate the change in the future population in drylands. According to Figure 8 both b and d in, it can be obtained that the increase in the dryland area has led to the growth of the population in drylands, and it is in a continuous growth state. However, compared with the change in the dryland area, the impact of population density on the growth of the population in drylands is obviously greater. Therefore, the change in population density has a greater effect on the change in the population in drylands. And it can be found that after the temperature rises by 3.5 °C, except for the SSP370 scenario, the population in drylands in other scenarios will decrease due to the decrease in population density, indicating that at a certain temperature rise level, drylands will become uninhabitable.

[0143] The beneficial effects of the present invention are:

[0144] (1) This invention makes up for the lack of quantitative detection and attribution research on the area of drylands in previous studies, and this invention quantifies the effects of different external forcings on the change of dryland area. This invention uses multiple sets of observational and reanalysis data, reducing the uncertainty in the research process. At the same time, through methods based on correlation analysis and the optimal fingerprint method, analysis is carried out on the basis of these two methods to detect the signals of external forcings and quantitatively attribute them, making the results more credible. Moreover, this invention is applicable to the global or individual regions.

[0145] (2) By differentiating the changes in the dryland population, this invention differentiates the changes in the dryland population caused by changes in population density and the changes in population caused by changes in drylands, which helps to better understand the changes in the dryland population and provides theoretical support for decision-makers to adapt to and mitigate the impact of climate change on the population.

Claims

1. A method for identifying warming signals of arid area expansion and assessing population impacts, characterized in that: It includes the following steps: Step S1: Data collection; collect precipitation and potential evapotranspiration data of global or regional observations or reanalysis, or variables capable of calculating potential evapotranspiration; collect precipitation, latent heat flux, sensible heat flux, relative humidity, wind speed, maximum temperature, minimum temperature and elevation data of the sixth phase of the Coupled Model Intercomparison Project CMIP6; Step S2: Calculate potential evapotranspiration and drought index; use the Penman-Monteith formula recommended by the Food and Agriculture Organization of the United Nations, combined with the data obtained in Step S1, to calculate potential evapotranspiration; combine with precipitation and use the ratio of precipitation to potential evapotranspiration to calculate the drought index used to characterize atmospheric drought; Step S3: Spatial patterns of wet-dry changes and their driving factors; combine the precipitation data obtained in Step S1 and the potential evapotranspiration and drought index calculated in Step S2 to calculate the changes in these three variables at spatial grid points in recent years relative to history, reflecting its drought spatial change pattern; Compare the correlation coefficient between the pre-industrial revolution experiment and the average observation with the Spearman correlation coefficients of historical climate simulation experiments, greenhouse gas forcing experiments, natural forcing experiments and average observations, and combine with the spatial changes of wet and dry to analyze the driving factors of drought changes; Step S4: Temporal patterns of wet-dry changes and their driving factors; combine the drought index obtained in Step S2, divide into historical scenarios and future scenarios, obtain the time series of anomalies of the drought index, compare the CMIP6 experiments with the observed average, and analyze the temporal change patterns and their driving factors; Step S5: Temporal change pattern of dryland area; define the area where the drought index is less than a as the drought area, combine the drought index obtained in Step S2, identify the grid points where the drought index is less than a, obtain the time series of the total annual drought area, and use the reference period to calculate the anomaly value, and finally obtain the anomaly time series of the drought area; where a is a preset value, and the value of a is determined according to different definitions of the drought index; where the reference period refers to a preset period of time; Compare and analyze the trend change values of observations and various CMIP6 experiments, and combine with the results obtained in Step S4 to analyze the temporal change pattern of the dryland area; Step S6: Detection and attribution of the temporal change of the dryland area; combine the correlation coefficient of the five-year average time series of the pre-industrial revolution drought area and the observed drought area, and compare the correlation coefficients of historical climate model experiments, greenhouse gas forcing experiments and natural forcing experiments with the observed values, analyze which external forcing simulation experiment the observed change of the dryland area is more consistent with, and analyze the influencing factors of the temporal change of the dryland area; Use the optimal fingerprint method of univariate and bivariate to detect and attribute external forcing factors; Step S7: Population change pattern in the drought area; combine the temperature data obtained in Step S1 and use the reference period to calculate the temperature increase level in the prediction period; where the prediction period also refers to a preset period of time, and the prediction period is after the reference period; Combined with the drought index obtained in step S2 and the population data obtained in step S1, the changes in the drought area and the population in the drought area are corresponded to the level of temperature rise, and the 20-year moving average sequences of the drought area and population under different warming levels are obtained. Analyze the relationship between the change in the drought area and the change in population, and the relationship between population, drought area and warming level; Step S8: Attribution of population change in the drought area; divide the population change in the drought area into the population affected by population density and the population affected by both the change in drought area and population density; Combined with the drought index obtained in step S2, and the population data and temperature data obtained in step S1, obtain the 20-year moving average time series of the population in the prediction period of the two parts under different warming levels; Compare and analyze the population change patterns of the two parts of the population change under different future scenarios at different warming levels; analyze the main factors affecting the population change in the drought area, and analyze the contribution rates of the change in population density and the combined effect of the change in drought area and population density.

2. The method for identifying warming signals of arid area expansion and assessing population impacts according to claim 1, wherein: In step S1, the observed or reanalyzed data are multiple sets of different precipitation and potential evapotranspiration data, and each set of data and the average value of multiple sets of data are analyzed.

3. The method for identifying warming signals of arid area expansion and assessing population impacts according to claim 1, wherein: In step S2, the Penman-Monteith formula is used to calculate the potential evapotranspiration, and the calculation formula is as follows: where PET represents potential evapotranspiration, R n represents net surface radiation, G represents soil heat flux density, T represents the daily average air temperature at 2 m height, u2 represents the wind speed at 2 m height, e s represents saturation vapor pressure, e a represents actual vapor pressure, Δ represents the slope of the vapor pressure curve, γ represents the psychrometric constant; where e s is calculated from the maximum and minimum air temperatures, and the formula is as follows: e a It can be calculated from the relative humidity and the saturated water vapor pressure, and the formula is as follows: R n obtained by the following formula: R n = R ns - R nl where R ns is the incident net shortwave radiation, and R nl is the outgoing net longwave radiation; R n -G is calculated by the following formula: R n -G = LH + SH where LH is the latent heat flux and SH is the sensible heat flux; Based on the calculated potential evapotranspiration, use the drought index to characterize atmospheric drought, and the formula is as follows: AI = P / PET where P represents precipitation and PET represents potential evapotranspiration. When AI is less than b, it means that the area is a drought area; b is a preset threshold.

4. The method for identifying warming signals of arid area expansion and assessing population impacts according to claim 1, wherein: Step S3 is specifically as follows: S31: Divide two different periods, the recent period and the historical period, and use the multi-year average values of the drought index, precipitation and potential evapotranspiration in the recent years to subtract the multi-year average values in the historical period to reflect the changes in the drought index, precipitation and potential evapotranspiration in the two different periods, as follows: ΔAI = AI pres -AI past where AI pres represents the multi-year average drought value of each grid point in recent years, and AI past represents the multi-year average drought value of each grid point in the historical period; S32: Perform multi-set or multi-model averaging on the observed and reanalyzed data, historical climate simulation experiments, greenhouse gas forcing experiments and natural forcing experiments to obtain the spatial distributions in different periods and different scenarios, and then subtract the historical period from the recent period to finally obtain the changing spatial distribution; S33: Compare the change directions and amounts of potential evapotranspiration, precipitation and drought index, and analyze the roles of precipitation and potential evapotranspiration in affecting the drought index; Based on the wet-dry space, calculate the Spearman correlation coefficient between the observed spatial change and the model-simulated spatial change, compare the positive and negative changes and the magnitudes of the correlation coefficients between the observed and reanalyzed data and the historical climate simulation experiments, greenhouse gas forcing experiments and natural forcing experiments, analyze the main external forcings affecting the observed data, and obtain the driving factors of the wet-dry change spatial pattern.

5. The method for identifying warming signals and assessing population impacts of arid area expansion according to claim 1, wherein: The formula for the optimal fingerprint method in step S6 is as follows: y = (X - α)β + ∈ where y represents the observed time series of dryland, X represents the simulated time series of dryland area, including historical climate experiments, greenhouse gas forcing and natural forcing; β represents the scale factor; ∈ represents the regression residual; Quantify the attribution of the observed changes in dryland to external forcing as: Con = Slope × β where slope represents the linear trend of the simulated dryland under various external forcings.

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