Land-air composite drought event prediction method and device

Abstract

The present application discloses a method and device for predicting land-air composite drought events. The comparison results between the grid-point land water storage series under each global climate model and the grid-point land water storage series of the GTWS-MLrec reconstructed data set determine the basin-wide comprehensive KGE index, and the global climate model with the largest basin-wide comprehensive KGE index is determined as the target global climate model, thus ensuring that the land drought intensity has a high degree of accuracy. An emergence constraint model is established through the change trend of the historical growing season monthly average temperature output by the target global climate model and the change trend of the normalized land-air drought index output by the target global climate model in the future period, and based on the emergence constraint model, the change trend of the corrected future period DI is determined by the change trend of the historical growing season monthly average temperature of the ERA5-Land data set, thereby improving the prediction accuracy of the evolution status.

Description

Technical Field

[0001] The present application relates to the technical field of climate data analysis, and in particular to a method and device for predicting drought-flood sudden-change events. Background Art

[0002] Since the Industrial Revolution, human economic activities have developed rapidly, emitting a large amount of greenhouse gases and aerosol particles, changing the radiation balance of the earth system, and causing a rapid rise in global temperature. The global "warming" effect has changed the thermal and dynamic environment of the atmospheric boundary layer, and has had a significant impact on meteorological and hydrological elements such as precipitation, atmospheric humidity, evapotranspiration, runoff, and terrestrial water storage. The contradiction of water shortage may be further aggravated in the future. At the same time, climate warming has changed the structure, function, carbon source and sink characteristics of global terrestrial ecosystems and other biogeochemical cycle processes. This change will have a feedback effect on the global climate, slowing down or aggravating climate warming. China is one of the countries most affected by climate change. The rapid rise in temperature may cause the frequency and intensity of drought events to continue to increase in the future, threatening national water security, food security and sustainable development of ecosystems. In order to meet the above challenges, it is urgent to strengthen scientific research on the evolution mechanism of drought under climate change.

[0003] Drought can generally be divided into meteorological drought, agricultural drought, hydrological drought and socio-economic drought. Meteorological drought is the basis of the other three types of drought. Its continued development may lead to insufficient soil moisture, resulting in a drop in river runoff, lake and reservoir water levels and groundwater levels, and even affecting human water use, which in turn leads to agricultural drought, hydrological drought and socio-economic drought. In the past, ground stations were generally used to monitor drought, which had defects such as uneven station distribution and lack of regional data. In recent years, satellite telemetry and data inversion technology have developed rapidly. Quantitative observation products based on GRACE and its successor GRACE-FO gravity satellite provide a wider coverage and higher temporal and spatial resolution, effectively making up for the shortcomings of insufficient ground station network layout, and providing data reference for drought monitoring in areas without data. Terrestrial water storage is the sum of continental water stored in tree canopies, ice and snow, rivers, lakes and reservoirs, wetlands, soil and groundwater, representing the total available water on land. In recent years, scholars have begun to try to use GRACE / GRACE-FO inversion data to assess droughts, and found that the terrestrial water storage anomaly-drought severity index (TWSA-DSI) based on terrestrial water storage anomalies can not only effectively monitor drought events, but also capture the spatiotemporal changes of surface fluxes on a large scale. Although GRACE / GRACE-FO satellites have begun to be used for drought monitoring and assessment, the current research on drought prediction based on TWSA-DSI at home and abroad is still in its infancy, and it is not clear how droughts will evolve in the future.

[0004] At the regional and global scales, drought is one of the most important stressors affecting the carbon sink function of terrestrial ecosystems. Some scholars have used satellite and flux station data to evaluate the impact of drought on vegetation carbon budget and found that extreme drought caused a significant decrease in total primary productivity and ecosystem net productivity. Some scholars have found that global temperature and saturated water vapor pressure deficit may continue to increase in the future, causing more frequent and more intense drought disasters and having a significant impact on vegetation carbon budget.

[0005] It is not difficult to find that it is difficult to accurately predict the evolution trend of land-air composite drought events using relevant technologies. Summary of the invention

[0006] In view of this, the land-air composite drought event prediction method and device of the present application can accurately predict the evolution of land-air composite drought events.

[0007] In a first aspect, the present application provides a land-air composite drought event prediction method, comprising:

[0008] The basin comprehensive KGE index under each global climate model is determined based on the comparison results between the grid terrestrial water storage series under each global climate model and the grid terrestrial water storage series of the GTWS-MLrec reconstructed dataset;

[0009] The global climate model with the largest basin-wide comprehensive KGE index is determined as the target global climate model, and the terrestrial water storage series corresponding to the target global climate model is obtained;

[0010] Determine the land drought intensity of the target global climate model according to the land water storage series corresponding to the target global climate model;

[0011] Based on the vegetation-land-atmosphere drought regression model, determining a normalized land-atmosphere drought index according to the land drought intensity and the land-atmosphere drought intensity of the target global climate model;

[0012] An emergence constraint model is constructed based on the trend of the average monthly temperature during the growing season in the historical period output by the target global climate model and the trend of the normalized land-air drought index in the future period output by the target global climate model;

[0013] Based on the emergence constraint model, the changing trend of the normalized land-air drought index in the future period after correction is determined according to the changing trend of the average monthly temperature in the growing season in the historical period of the ERA5-Land dataset, and the evolution of the land-air composite drought event is predicted based on the changing trend of the normalized land-air drought index in the future period after correction.

[0014] Optionally, determine the comprehensive KGE index of the basin under each global climate model, including:

[0015] Determine the KGE index of each grid point in the global climate model according to the comparison result;

[0016] The comprehensive KGE index of the watershed is determined according to the KGE index of each grid point.

[0017] Optionally, the KGE index for each grid point in the global climate model is determined by the following formula,

[0018] ;

[0019] In the above formula, r represents the Pearson linear correlation coefficient between the global climate model series and the reconstruction series; α represents the ratio of the variance of the simulated series to the reconstructed series; β Represents the ratio of the means of the simulated series to the reconstructed series.

[0020] Optionally, the comprehensive KGE index of the watershed is determined according to the KGE index of each grid point, and is performed by the following formula:

[0021] ;

[0022] In the above formula, KGE For the i Basin-wide comprehensiveness of global climate models KGE index, For the i The global climate model gg KGE index of each grid point; For the gg The area weight of each grid point is gg The cosine value of the latitude of the center of each grid point.

[0023] Optionally, determine the land water storage series for each grid point in the global climate model, specifically:

[0024] Determine the independence weight parameters of each artificial intelligence model, as expressed in the following formula:

[0025] ;

[0026] ;

[0027] In the above formula, Represents the Euclidean difference of different AI models, represents the similarity radius;

[0028] Determine the skill weight parameters of each artificial intelligence model, as expressed in the following formula:

[0029] ;

[0030] In the above formula, Representing an AI model i The error relative to the actual observation is represents the model mass radius;

[0031] Based on the independence weight parameter and the skill-based weight parameter, a terrestrial water storage series is determined.

[0032] Optionally, the determination of the land-atmosphere drought intensity of the target global climate model comprises:

[0033] Based on the Clausius-Clapeyron thermodynamic equation, the saturated vapor pressure deficit is determined by the monthly average temperature and relative humidity of the target global climate model;

[0034] The intensity of atmospheric drought over land is determined based on the saturated water vapor pressure deficit.

[0035] Optionally, the land drought intensity of the target global climate model is determined, specifically, based on the monthly land water storage inverted by gravity satellites, the mean of the land water storage for multiple months, and the standard deviation of the land water storage for multiple months.

[0036] In a second aspect, the present application provides a land-air composite drought event prediction device, comprising:

[0037] The first determination module is used to determine the basin comprehensive KGE index under each global climate model based on the comparison results between the grid point terrestrial water storage series under each global climate model and the grid point terrestrial water storage series of the GTWS-MLrec reconstructed data set;

[0038] An acquisition module is used to determine the global climate model with the largest basin comprehensive KGE index as the target global climate model, and obtain the terrestrial water storage series corresponding to the target global climate model;

[0039] A second determination module is used to determine the land drought intensity of the target global climate model according to the land water storage series corresponding to the target global climate model;

[0040] A third determination module is used to determine a normalized land-atmosphere drought index based on the vegetation-land-atmosphere drought regression model according to the land drought intensity and the land-atmosphere drought intensity of the target global climate model;

[0041] A construction module is used to construct an emergence constraint model according to the change trend of the average monthly temperature in the growing season in the historical period output by the target global climate model and the change trend of the normalized land-air drought index in the future period output by the target global climate model;

[0042] A prediction module is used to determine the changing trend of the normalized land-air drought index in the future period after correction based on the emergence constraint model and the changing trend of the monthly average temperature in the growing season in the historical period of the ERA5-Land data set, and predict the evolution of the land-air composite drought event based on the changing trend of the normalized land-air drought index in the future period after correction.

[0043] In a third aspect, the present application provides a computer-readable storage medium, comprising a program, which, when executed on a computer, enables the computer to execute the method as described above.

[0044] In a fourth aspect, the present application provides an execution device, comprising a processor and a memory, wherein the processor is coupled to the memory;

[0045] The memory is used to store programs;

[0046] The processor is used to execute the program in the memory, so that the execution device executes the above method.

[0047] In the method disclosed in the present application, the comparison results between the grid terrestrial water storage series of each global climate model and the grid terrestrial water storage series of each grid in the GTWS-MLrec reconstructed data set determine the basin-wide comprehensive KGE index, and the global climate model with the largest basin-wide comprehensive KGE index is determined as the target global climate model, so as to ensure that the land drought intensity determined by the land water storage series has a high degree of accuracy. Based on this, an emergent constraint model is generated through the change trend of the historical growing season monthly average temperature output by the target global climate model and the change trend of the normalized land-air drought index (DI) output by the target global climate model in the future period, and the change trend of the corrected future DI is determined based on the emergent constraint model by the change trend of the historical growing season monthly average temperature of the ERA5-Land data set, so as to characterize the future evolution of the land-air composite drought event through the DI change trend, thereby improving the prediction accuracy of the evolution status. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0049] Figure 1 An operational flow chart of a land-air composite drought event prediction method provided by an exemplary embodiment is shown.

[0050] Figure 2 A block diagram of a land-air composite drought event prediction device provided by an exemplary embodiment is shown;

[0051] Figure 3 A block diagram of the execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0053] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of units in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between units can be electrical or other similar forms, which are not limited in this application. In addition, the units or sub-units described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed in multiple circuit units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present application.

[0054] Please refer to Figure 1 , Figure 1 A flowchart of a land-air composite drought event prediction method provided by an exemplary embodiment is shown. The method is implemented through steps 101-106.

[0055] In step 101, the basin comprehensive KGE index under each global climate model is determined based on the comparison results between each grid point terrestrial water storage series under each global climate model and each grid point terrestrial water storage series of the GTWS-MLrec reconstructed data set.

[0056] As an example, the comprehensive KGE index of the river basin under each global climate model is determined, including:

[0057] Determine the KGE index of each grid point in the global climate model according to the comparison result;

[0058] The comprehensive KGE index of the watershed is determined according to the KGE index of each grid point.

[0059] As an exemplary process for determining the KGE index of each grid point, the following formula can be used:

[0060] ;

[0061] In the above, r represents the Pearson linear correlation coefficient between the global climate model series and the reconstruction series; α represents the ratio of the variance of the simulated series to the reconstructed series; β Represents the ratio of the mean of the global climate model series to the reconstructed series.

[0062] It is worth adding that KGE The range of the efficiency coefficient is (-∞, 1). When KGE=1, it means that the simulation series is completely consistent with the reconstructed series.

[0063] As an example, the comprehensive KGE index of the watershed is determined according to the KGE index of each grid point, which is performed by the following formula:

[0064] ;

[0065] In the above, KGE For the i Basin-wide comprehensiveness of global climate models KGE index, For the i The global climate model gg KGE index of each grid point; For the gg The area weight of each grid point is gg The cosine value of the latitude of the center of each grid point.

[0066] As a feasible approach, the determination of the land water storage series of each grid point under each global climate model can be comprehensively determined based on the ENSO index and the monthly average 2m temperature, monthly precipitation, monthly snowfall, monthly runoff depth, and monthly shortwave radiation intensity output by the global climate model.

[0067] As used in the text, the term "ENSO index" refers to the time series of the El Nino Southern Oscillation (ENSO) index, which can be the three ENSOs from 1950 to the present, specifically including the NINO 1+2 region sea surface temperature anomaly index, the NINO 3 region sea surface temperature anomaly index, and the NINO 3.4 region sea surface temperature anomaly index.

[0068] The ENSO index for the historical and future periods used in this paper can be calculated based on the global sea surface temperature data for the historical period (1940-2014) and the future period simulated by 36 global climate models under the SSP5-85 shared socioeconomic pathway. The calculation of the ENSO index is a conventional technique in the field, and the calculation of the global sea surface temperature data (2030-2100) will not be repeated here.

[0069] Here, the monthly mean 2m temperature, monthly precipitation, monthly snowfall, monthly runoff depth, and monthly shortwave radiation intensity are calculated from the daily series output by the global climate model, namely, the daily mean 2m temperature, daily precipitation, daily snowfall, daily runoff depth, and daily shortwave radiation intensity, respectively.

[0070] Here, the global climate model may specifically include five global climate models: M6A-LR model, GFDL-ESM4 model, MPI-ESM1-2-HR model, MRI-ESM2-0 model, and UKESM1-0-LL model.

[0071] The determination of the land water storage series in the previous article is based on the ENSO index and the monthly average 2m temperature, monthly precipitation, monthly snowfall, monthly runoff depth, and monthly shortwave radiation intensity output by the global climate model. The method for determining the land water storage series with these parameters can adopt machine learning methods.

[0072] Specifically, based on multiple artificial intelligence models and the weights corresponding to each artificial intelligence model, the terrestrial water storage series is determined.

[0073] The machine learning simulation method of the related technology generally calculates the regional average meteorological variables first, and then uses them to calibrate the artificial intelligence model. This application uses the meteorological and hydrological variables of all grid points in the study area as input, which is more in line with the physical driving mechanism of terrestrial water storage; the number of artificial intelligence models here can be exemplified as 5 artificial intelligence models, including but not limited to convolutional neural networks, long short-term memory models, support vector machines, random forests and artificial neural networks; the gradient descent method is used to train the 5 artificial intelligence models to obtain the calibrated 5 artificial intelligence models. The calibration method of the artificial intelligence model is a conventional technology in the field.

[0074] For the five artificial intelligence models, the weight parameters of the multi-model weighted average method are derived. Specifically, they include:

[0075] There are January to December in a year. In the weight derivation scheme, the weight of each month in different years is the same, so the weights of January to December need to be deduced separately, as follows:

[0076] For each month, the weight parameters of the 5 AI models satisfy:

[0077] ;

[0078] in, i Indicates the serial number of the artificial intelligence model; Indicates i The weight parameters of the AI ​​model ；M Indicates that the number of AI models is 5;

[0079] Determine the independence weight parameters of each artificial intelligence model, as expressed in the following formula:

[0080] ;

[0081] ;

[0082] In the above, represents the Euclidean difference between different AI models, which can be obtained, for example, by normalizing the average difference between simulated terrestrial water storage under AI models i and j; It represents the similarity radius, for example, obtained by iteratively calculating the difference between the simulated terrestrial water storage and the measured terrestrial water storage under the optimal combination scenario. Indicates the similarity between the current AI model and other AI models. The value range can be .

[0083] Determine the skill weight parameters of each artificial intelligence model, as expressed in the following formula:

[0084] ;

[0085] In the above, Representing an AI model i The error relative to actual observations, for example, normalized by the root mean square error of the simulated terrestrial water storage relative to the measured terrestrial water storage; represents the model mass radius, for example, obtained by calculating the difference between the simulated terrestrial water storage and the measured terrestrial water storage under the optimal combination scenario. When it is close to 0, only the optimal AI model has a high skill weight.

[0086] In step 102, the global climate model with the largest basin-wide comprehensive KGE index is determined as the target global climate model, and the terrestrial water storage series corresponding to the target global climate model is obtained.

[0087] It can be understood that in the step, the actual operation is to select the one with the largest basin comprehensive KGE index from the five global climate models under the SSP5-85 shared socioeconomic pathway and define it as the target global climate model.

[0088] In step 103, the land drought intensity of the target global climate model is determined according to the land water storage series corresponding to the target global climate model.

[0089] The determination of the land-atmosphere drought intensity of the target global climate model as an example includes:

[0090] A. Based on the Clausius-Clapeyron thermodynamic equation, the vapor pressure deficit (VPD) is determined from the monthly average temperature and relative humidity of the target global climate model;

[0091] For example, the derived formula based on the Clausius-Clapeyron thermodynamic equation is as follows:

[0092] Where: VdP The first j Months of VPD data; T j and RH j Respectively j Monthly temperature and relative humidity. Here, is the Clausius-Clapeyron thermodynamic equation, which is defined as follows:

[0093] ;

[0094] in, is the first integration constant, which is taken as 273.16 K; is the second integral constant, which is 611 Pa; is the latent heat of vaporization constant, taken as 2.5×10 6 J·kg -1 ; is the water vapor gas constant, which is 461 J·kg -1 ·K -1 ; T is the air temperature, which is the input variable of the Clausius-Clapeyron thermodynamic equation.

[0095] B. Determine the intensity of atmospheric drought over land based on the saturated water vapor pressure deficit.

[0096] The specific principles or processes for determining the intensity of land-atmosphere drought can be found in the construction of the vegetation-land-atmosphere drought regression model, which will not be elaborated here.

[0097] In step 104, based on the vegetation-land-atmosphere drought regression model, a normalized land-atmosphere drought index is determined according to the land drought intensity and the land-atmosphere drought intensity of the target global climate model.

[0098] As an example, the construction of the vegetation-land-atmosphere drought regression model includes:

[0099] Establish vegetation growing season based on satellite-derived vegetation data;

[0100] The saturated vapor pressure deficit (VPD) is determined based on the relative humidity of each grid point inferred from the 2-meter air temperature and dew point temperature of the meteorological and hydrological observation samples of each grid point in the land-air composite drought prediction area;

[0101] Based on the saturated vapor pressure deficit (VPD), the atmospheric drought intensity (VPDD) is derived;

[0102] Based on the land water storage data inverted from gravity satellites, the land drought intensity is estimated;

[0103] According to the atmospheric drought intensity and the terrestrial drought intensity, and based on the Thiessen polygon method, a vegetation-land-atmosphere drought regression model in the growing season is constructed.

[0104] The above process of determining the vegetation growing season may include:

[0105] A. Using the hourly meteorological and hydrological observation data of the fifth generation atmospheric reanalysis data set (ERA5) of the European Center for Medium-Range Weather Forecasts, the monthly average 2m air temperature, monthly average dew point temperature, monthly precipitation, monthly snowfall, monthly runoff depth, monthly shortwave radiation intensity, and monthly average leaf area index (LAI) of each grid point were calculated in turn. Then, the monthly scale LAI, solar-induced chlorophyll fluorescence data (SIFD), and NDVI (Normalized Difference Vegetation Index) data after regional spatial averaging were calculated using the Thiessen polygon method.

[0106] B. The mean of LAI, SIFD and NDVI was used to characterize the vegetation growth state (VEG), and the five consecutive months with the highest VEG were identified and defined as the growing season of vegetation.

[0107] It should be known that sunlight-induced chlorophyll fluorescence (SIF) refers to a type of fluorescence with a wavelength of 650-800nm ​​emitted by chloroplasts of plants when they absorb photosynthetically active radiation under natural sunlight. When sunlight shines on the surface of plants, it will be reflected, transmitted, and absorbed by leaves in three ways. After the leaves absorb light and effective radiation, the energy obtained is not all used for photosynthesis, but also releases energy in the form of fluorescence and heat dissipation. The three energy release methods compete with each other, and any change in one energy will lead to changes in the other two energies. Therefore, the strength of SIF is related to the photosynthetic capacity, and SIF is also called a probe of photosynthesis.

[0108] In actual operation, the above Normally, standardization is required. The process of obtaining the standardized SIF series is:

[0109] ;

[0110] Where: Indicates j Months of standardized SIF data, represents the original SIF data of the jth month; and Respectively m The mean and standard deviation of the monthly SIF, m =1,2…,12, according to j Determine the calendar month in which the month falls.

[0111] Here, the NDVI dataset is the monthly average NDVI data obtained by averaging the data of the first 15 days and the last 15 days of each month from the satellite inversion data and then reconstructing it using the maximum synthesis method.

[0112] The saturated vapor pressure deficit determined in the previous article is based on the Clausius-Clapeyron thermodynamic equation, as follows:

[0113] ;

[0114] in, e sat (·) represents the Clausius-Clapeyron thermodynamic equation; represents the saturated vapor pressure deficit at the g-th longitude and latitude coordinate point in the j-th month, It represents the 2-meter temperature at the g-th latitude and longitude coordinate point in the j-th month. represents the dew point temperature at the g-th latitude and longitude coordinate point in the j-th month of the reconstruction period;

[0115] The process of calculating the land drought intensity in the previous article can be expressed by the following formula:

[0116] ;

[0117] in, Representative j Monthly drought intensity based on terrestrial water storage anomalies, Represents the first j Months of land water storage data, Respectively m The mean and standard deviation of monthly TWSA, m =1,2…,12, according to j Determine the calendar month in which the month falls.

[0118] Regarding the terrestrial water storage data inverted by gravity satellites, satellite gravity data is used to invert terrestrial water storage. Satellite gravity data is used to invert terrestrial water storage. Internationally, the main institutions responsible for data interpretation include the Potsdam Geoscience Center in Germany, the Jet Propulsion Laboratory (JPL) of the California Institute of Technology, the Center for Space Research at University of Texas, Austin (CSR) and the Goddard Space Flight Center (GSFC) of NASA, and the like, which publish the global gravity field output data on a monthly scale. The present invention simultaneously uses the latest sixth-generation (RL06) products released by JPL, CSR and GSFC. These three data sources have different spatial resolutions, and all provide mascon monthly-scale equivalent water heights (minus the average field from 2004 to 2009) based on the concentrated mass block gravity field solution, and finally output a long sequence of terrestrial water storage data sets. In order to consider the uncertainties that may be caused by different data products, the three sets of GRACE / GRACE-FO gravity satellite datasets were interpolated to a 0.25°×0.25° spatial grid, and the average of each product was taken in each time step, finally obtaining the monthly dataset of terrestrial water storage from 2002 to the present; further, the high-precision terrestrial water storage dataset from 1940 to 2022 developed by Wuhan University was obtained, and the 0.25°×0.25° terrestrial water storage data covering the study area were extracted from this dataset, recorded as GTWS-Mlrec.

[0119] The process of deriving the atmospheric drought intensity (VPDD) in the previous article can be expressed by the following formula:

[0120] ;

[0121] Where: VPD j The first global climate model simulation j Months of VPD data; T j and RH j are the air temperature and relative humidity of the jth month respectively.

[0122] The vegetation-land-atmosphere drought regression model in the growing season can be represented by the following formula:

[0123] ;

[0124] in, is the LAI anomaly of the mth month relative to the entire growing season, is the NDVI anomaly of the mth month relative to the entire growing season, is the SIFD anomaly of the mth month relative to the entire growing season; For the m Monthly TWSA-DSI index; For the m Month Index; α and β are model parameters, and the least squares method is used to solve these two parameters.

[0125] In step 104, a normalized land-air drought index is determined according to the land drought intensity and the land-atmosphere drought intensity of the target global climate model, which can be expressed by the following formula:

[0126] ;

[0127] Where: DI is the normalized land-atmosphere drought index; VPDD and TWSA-DSI are the atmospheric drought intensity and land drought intensity simulated by the global climate model, respectively.

[0128] In step 105, an emergence constraint model is constructed based on the trend of the average monthly temperature during the growing season in the historical period output by the target global climate model and the trend of the normalized land-air drought index in the future period output by the target global climate model.

[0129] In this step 105, the emergence constraint model is expressed by the following formula:

[0130] ;

[0131] Where: Indicates the DI index change trend of the kth grid point in the future period, It indicates the trend of the average monthly temperature during the growing season in the historical period. a and b Represents model parameters.

[0132] The least square method is used to solve the parameters a and b of the emergence constraint model.

[0133] In step 106, based on the emergence constraint model, the changing trend of the normalized land-air drought index in the future period after correction is determined according to the changing trend of the average monthly temperature in the growing season in the historical period of the ERA5-Land data set, and the evolution of the land-air composite drought event is predicted based on the changing trend of the normalized land-air drought index in the future period after correction.

[0134] The changing trend of the normalized land-air drought index in the future period after correction can be expressed by the following formula:

[0135] ;

[0136] Where: CorDItrend k Indicates k The change trend of the DI index after correction at each grid point in the future period, OBSTtrend k Indicates k The changing trend of the average monthly temperature during the growing season in the ERA5-Land dataset at each grid point during the historical period.

[0137] Please refer to Figure 2 , which shows a block diagram of the land-air composite drought event prediction device provided by the present application. The device 200 includes:

[0138] The first determination module 201 is used to determine the basin comprehensive KGE index under each global climate model according to the comparison results between each grid point terrestrial water storage series under each global climate model and each grid point terrestrial water storage series of the GTWS-MLrec reconstructed data set;

[0139] An acquisition module 202 is used to determine the global climate model with the largest basin comprehensive KGE index as the target global climate model, and obtain the land water storage series corresponding to the target global climate model;

[0140] A second determination module 203 is used to determine the land drought intensity of the target global climate model according to the land water storage series corresponding to the target global climate model;

[0141] The third determination module 204 is used to determine a normalized land-atmosphere drought index according to the land drought intensity and the land-atmosphere drought intensity of the target global climate model based on the vegetation-land-atmosphere drought regression model;

[0142] A construction module 205 is used to construct an emergence constraint model according to the change trend of the average monthly temperature in the growing season in the historical period output by the target global climate model and the change trend of the normalized land-air drought index in the future period output by the target global climate model;

[0143] The prediction module 206 is used to determine the changing trend of the normalized land-air drought index in the future period after correction based on the emergence constraint model and the changing trend of the average monthly temperature in the growing season in the historical period of the ERA5-Land data set, and predict the evolution of the land-air composite drought event based on the changing trend of the normalized land-air drought index in the future period after correction.

[0144] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0145] Next, an execution device provided by an embodiment of the present application is introduced. Figure 3, Figure 3 This is a schematic diagram of a structure of an execution device provided in an embodiment of the present application. The execution device 300 can be specifically manifested as an autonomous driving vehicle, a mobile phone, a tablet, a laptop computer, a desktop computer, a monitoring data processing device, etc., which is not limited here. Figure 1 The functions of the execution device in the corresponding embodiment. Specifically, the execution device 300 includes: a receiver 301, a transmitter 302, a processor 303 and a memory 304 (wherein the number of the processor 303 in the execution device 300 can be one or more, Figure 3 In the example of FIG. 3 , the processor 303 may include an application processor 3031 and a communication processor 3032. In some embodiments of the present application, the receiver 301, the transmitter 302, the processor 303 and the memory 304 may be connected via a bus or other means.

[0146] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 303. A portion of the memory 304 may also include a non-volatile random access memory (NVRAM). The memory 304 stores processor and operation instructions, executable modules or data structures, or subsets thereof, or extended sets thereof, wherein the operation instructions may include various operation instructions for implementing various operations.

[0147] The processor 303 controls the operation of the execution device. In a specific application, the various components of the execution device are coupled together through a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus, and a status signal bus, etc. However, for the sake of clarity, various buses are referred to as bus systems in the figure.

[0148] The method disclosed in the above embodiment of the present application can be applied to the processor 303, or implemented by the processor 303. The processor 303 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 303. The above processor 303 can be a general-purpose processor, a digital signal processor (digital signal processing, DSP), a microprocessor or a microcontroller, and can further include an application specific integrated circuit (application specific integrated circuit, ASIC), a field programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The processor 303 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 304, and the processor 303 reads the information in the memory 304 and completes the steps of the above method in combination with its hardware.

[0149] The receiver 301 can be used to receive inputted digital or symbol information, and generate signal input related to the relevant settings and function control of the execution device. The transmitter 302 can be used to output the digital or symbol information through the first interface; the transmitter 302 can also be used to send instructions to the disk group through the first interface to modify the data in the disk group; the transmitter 302 can also include a display device such as a display screen.

[0150] In the embodiment of the present application, the processor 303 is used to execute Figure 1 The specific manner in which the application processor 3031 in the processor 303 performs the above steps is the same as that in the present application. Figure 1 The corresponding method embodiments are based on the same concept, and the technical effects they bring are the same as those in this application. Figure 1 The corresponding method embodiments are the same. For specific contents, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0151] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A land-air composite drought event prediction method, characterized in that: include: The basin comprehensive KGE index under each global climate model is determined based on the comparison results between the grid terrestrial water storage series under each global climate model and the grid terrestrial water storage series of the GTWS-MLrec reconstructed dataset; The global climate model with the largest basin-wide comprehensive KGE index is determined as the target global climate model, and the terrestrial water storage series corresponding to the target global climate model is obtained; Determine the land drought intensity of the target global climate model according to the land water storage series corresponding to the target global climate model; Based on the vegetation-land-atmosphere drought regression model, determining a normalized land-atmosphere drought index according to the land drought intensity and the land-atmosphere drought intensity of the target global climate model; An emergence constraint model is constructed based on the trend of the average monthly temperature during the growing season in the historical period output by the target global climate model and the trend of the normalized land-air drought index in the future period output by the target global climate model; Based on the emergence constraint model, the changing trend of the normalized land-air drought index in the future period after correction is determined according to the changing trend of the average monthly temperature in the growing season in the historical period of the ERA5-Land dataset, and the evolution of the land-air composite drought event is predicted based on the changing trend of the normalized land-air drought index in the future period after correction.

2. The method according to claim 1, characterized in that Determine the comprehensive KGE index of the basin under each global climate model, including: Determine the KGE index of each grid point in the global climate model according to the comparison result; The comprehensive KGE index of the watershed is determined according to the KGE index of each grid point.

3. The method according to claim 2, characterized in that The KGE index of each grid point in the global climate model is determined by the following formula: In the above formula, r represents the Pearson linear correlation coefficient between the global climate model series and the reconstruction series; α represents the ratio of the variance of the simulation series to the reconstruction series; β represents the ratio of the mean of the global climate model series to the reconstruction series.

4. The method according to claim 2, characterized in that: The comprehensive KGE index of the basin is determined according to the KGE index of each grid point, which is implemented by the following formula: In the above formula, KGE i is the comprehensive KGE index of the basin in the i-th global climate model, KGE i (gg) is the KGE index of the ith global climate model at the ggth grid point; cosd(Lat gg ) is the area weight of the gg-th grid point, which is represented by the cosine value of the latitude of the center of the gg-th grid point.

5. The method according to claim 1, characterized in that Determine the land water storage series for each grid point in the global climate model, specifically: Determine the independence weight parameters of each artificial intelligence model, as expressed in the following formula: In the above formula, δ i,j Represents the Euclidean difference of different AI models, D u represents the similarity radius; M represents the number of artificial intelligence models; Determine the skill weight parameters of each artificial intelligence model, as expressed in the following formula: In the above formula, δ i,obs represents the error of artificial intelligence model i relative to actual observation, D q represents the model mass radius; Based on the independence weight parameter and the skill-based weight parameter, a terrestrial water storage series is determined.

6. The method according to claim 1, characterized in that The determination of the land-atmosphere drought intensity of the target global climate model includes: Based on the Clausius-Clapeyron thermodynamic equation, the saturated vapor pressure deficit is determined by the monthly average temperature and relative humidity of the target global climate model; The intensity of atmospheric drought over land is determined based on the saturated water vapor pressure deficit.

7. The method according to claim 1, characterized in that The land drought intensity of the target global climate model is determined, specifically, based on the monthly land water storage inverted by the gravity satellite, the mean of the land water storage for multiple months and the standard deviation of the land water storage for multiple months.

8. A land-air composite drought event prediction device, characterized in that: include: The first determination module is used to determine the basin comprehensive KGE index under each global climate model based on the comparison results between the grid point terrestrial water storage series under each global climate model and the grid point terrestrial water storage series of the GTWS-MLrec reconstructed data set; An acquisition module is used to determine the global climate model with the largest basin comprehensive KGE index as the target global climate model, and obtain the terrestrial water storage series corresponding to the target global climate model; A second determination module is used to determine the land drought intensity of the target global climate model according to the land water storage series corresponding to the target global climate model; A third determination module is used to determine a normalized land-atmosphere drought index based on the vegetation-land-atmosphere drought regression model according to the land drought intensity and the land-atmosphere drought intensity of the target global climate model; A construction module is used to construct an emergence constraint model according to the change trend of the average monthly temperature in the growing season in the historical period output by the target global climate model and the change trend of the normalized land-air drought index in the future period output by the target global climate model; A prediction module is used to determine the changing trend of the normalized land-air drought index in the future period after correction based on the emergence constraint model and the changing trend of the monthly average temperature in the growing season in the historical period of the ERA5-Land data set, and predict the evolution of the land-air composite drought event based on the changing trend of the normalized land-air drought index in the future period after correction.

9. A computer-readable storage medium, characterized in that: The invention comprises a program, which, when being executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

10. An execution device, characterized in that: comprising a processor and a memory, wherein the processor is coupled to the memory; The memory is used to store programs; The processor is configured to execute the program in the memory so that the execution device executes the method according to any one of claims 1 to 7.

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