Heat wave-drought composite event prediction method and device

Abstract

The present application discloses a method and device for predicting heat wave-drought composite events. The method comprises: determining the terrestrial drought intensity under the global climate model based on the terrestrial water storage series under the global climate model, wherein the terrestrial water storage series is determined at least based on the saturated water vapor pressure deficit; determining a normalized comprehensive drought index based on the terrestrial drought intensity and the standardized runoff drought intensity under the global climate model based on the vegetation-drought regression model; determining a normalized high temperature index based on the daily average wet-bulb temperature of each grid point and the daily average pathogenic high temperature stress index under the global climate model based on the disease-high temperature relationship model during the vegetation growing season; predicting the occurrence status based on the normalized comprehensive drought index and the normalized high temperature index. In the method disclosed in the present application, atmospheric humidity is fully considered, and the impact of heat waves and drought disasters on human health and vegetation carbon fixation effect is comprehensively considered from the land surface and the atmosphere, thereby improving the accuracy of predicting the occurrence of heat wave-drought composite events.

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 heat wave-drought combined events. Background Art

[0002] 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. At regional and global scales, heat waves and droughts are one of the most important stress factors affecting the carbon sink function of terrestrial ecosystems. Some scholars have used satellite and flux station data to assess 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. Scholars also found that drought and heat wave events not only have a significant impact on ecosystem carbon budget, but also have a significant inhibitory effect on human health and social productivity.

[0003] In recent years, scholars at home and abroad have used the global climate model ensemble under the Sixth International Coupled Model Intercomparison Project in combination with river basin hydrological models and machine learning models to study the evolution of future high-temperature heat wave-drought complex disasters, and achieved certain application results.

[0004] However, it is difficult to accurately predict the occurrence of heat wave-drought combined events in related technologies. Summary of the invention

[0005] In view of this, the heat wave-drought combined event prediction method and device of the present application can accurately predict the occurrence of heat wave-drought combined events.

[0006] In a first aspect, the present application provides a method for predicting a heat wave-drought composite event, comprising:

[0007] Determine the intensity of land drought in the global climate model based on the land water storage series in the global climate model, the land water storage series is determined based on at least the saturated water vapor pressure deficit in the global climate model;

[0008] Based on the vegetation-drought regression model, a normalized comprehensive drought index is determined according to the land drought intensity under the global climate model and the normalized runoff drought intensity under the global climate model;

[0009] Based on the disease-high temperature relationship model during the vegetation growing season, the normalized high temperature index was determined according to the daily average wet-bulb temperature and daily average pathogenic high temperature stress index of each grid point under the global climate model;

[0010] It is used to predict the occurrence of heat wave-drought compound events according to the normalized comprehensive drought index and the normalized high temperature index.

[0011] Optionally, the determination of the terrestrial water storage series under the global climate model comprises:

[0012] The land water storage series is determined based on the ENSO index, the saturated vapor pressure deficit under the global climate model, the monthly average 2m temperature, monthly precipitation, monthly snowfall, monthly runoff depth, and monthly shortwave radiation intensity output by the global climate model.

[0013] Optionally, the saturated vapor pressure deficit under the global climate model is determined according to the average monthly temperature under the global climate model, the relative humidity under the global climate model, and based on the Clausius-Clapeyron thermodynamic equation.

[0014] Optionally, the terrestrial water storage series is determined as follows:

[0015] Based on multiple artificial intelligence models and the corresponding weights of each artificial intelligence model, the terrestrial water storage series is determined.

[0016] Optionally, the construction of the vegetation-drought regression model includes:

[0017] Establish vegetation growing season based on satellite-derived vegetation data;

[0018] The drought intensity is estimated based on the land water storage data inverted from gravity satellites;

[0019] Based on the monthly runoff depth data of ERA5, the standardized runoff drought intensity was derived;

[0020] According to the drought intensity, the standardized runoff drought intensity, and based on the Thiessen polygon method, a vegetation-land-atmosphere drought regression model in the growing season is constructed.

[0021] Optionally, the construction of the disease-high temperature relationship model during the vegetation growing season includes:

[0022] According to the hourly 2m air temperature and dew point temperature provided by the ERA5 dataset, the hourly wet bulb temperature of each grid point is calculated;

[0023] According to the hourly wet-bulb temperature and relative humidity, the hourly pathogenic high temperature stress index of each grid point is calculated;

[0024] Based on the hourly pathogenic high temperature stress index, a disease-heat wave association model for the vegetation growing season is established.

[0025] Optionally, forecast the occurrence of combined heatwave-drought events, including:

[0026] Determining the intensity of the heat wave-drought composite event according to the normalized comprehensive drought index and the normalized high temperature index;

[0027] The relative change status of the heat wave-drought compound event at each grid point in the global climate model is obtained from the intensity, and the uncertainty of the occurrence of the heat wave-drought compound event is represented by the standard deviation of the relative change status.

[0028] In a second aspect, the present application provides a heat wave-drought combined event prediction device, comprising:

[0029] A first determination module is used to determine the land drought intensity under the global climate model according to the land water storage series under the global climate model, wherein the land water storage series is determined at least according to the saturated water vapor pressure deficit under the global climate model;

[0030] A second determination module is used to determine a normalized comprehensive drought index based on a vegetation-drought regression model according to the land drought intensity under the global climate model and the standardized runoff drought intensity under the global climate model;

[0031] The third determination module is used to determine the normalized high temperature index based on the disease-high temperature relationship model during the vegetation growing season and the daily average wet-bulb temperature and daily average pathogenic high temperature stress index of each grid point under the global climate model;

[0032] The fourth determination module is used to predict the occurrence of a heat wave-drought composite event based on the normalized comprehensive drought index and the normalized high temperature index.

[0033] 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.

[0034] 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;

[0035] The memory is used to store programs;

[0036] The processor is used to execute the program in the memory, so that the execution device executes the above method.

[0037] In the method disclosed in the present application, atmospheric humidity is fully taken into account, and the impact of heat waves and drought disasters on human health and vegetation carbon fixation effect are comprehensively considered from the land surface and the atmosphere, thereby improving the accuracy of prediction of the occurrence of heat wave-drought complex events. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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.

[0039] Figure 1 An operational flow chart of a method for predicting drought-flood transition events provided by an exemplary embodiment is shown.

[0040] Figure 2 A block diagram of a drought-flood sudden change event prediction device provided by an exemplary embodiment is shown;

[0041] Figure 3 A block diagram of the execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0042] 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.

[0043] 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.

[0044] Please refer to Figure 1 , Figure 1 A flowchart of a heat wave-drought combined event prediction method provided by an exemplary embodiment is shown. The method is implemented through steps 102-108.

[0045] In step 102, the land drought intensity in the global climate model is determined according to the land water storage series in the global climate model, and the land water storage series is determined at least according to the saturated water vapor pressure deficit in the global climate model.

[0046] Here, the determination of the terrestrial water storage series can be based on the saturated water vapor pressure deficit under the global climate model, in addition to the ENSO index, the saturated water vapor pressure deficit under the global climate model, the monthly average 2m temperature, monthly precipitation, monthly snowfall, monthly runoff depth, and monthly shortwave radiation intensity output by the global climate model.

[0047] 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.

[0048] 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 five 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.

[0049] 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, monthly snowfall, daily runoff depth, and daily shortwave radiation intensity, respectively.

[0050] 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.

[0051] As an example, the saturated vapor pressure deficit (VPD) in the global climate model is determined according to the average monthly temperature in the global climate model, the relative humidity in the global climate model, and based on the Clausius-Clapeyron thermodynamic equation, for example, it can be expressed by referring to the following formula:

[0052] ;

[0053] Where: VPD j The first global climate model simulation j Months of VPD data; T j and RH j Respectively j Monthly temperature and relative humidity; Refers to the Clausius-Clapeyron thermodynamic equation.

[0054] Here, the Clausius-Clapeyron thermodynamic equation is defined as follows:

[0055] ;

[0056] 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.

[0057] The determination of the land water storage series in the previous article is based on the ENSO index, the saturated vapor pressure deficit in the global climate model, 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 of determining the land water storage series with these parameters can be machine learning.

[0058] Specifically, based on multiple artificial intelligence models and the weights corresponding to each artificial intelligence model, the terrestrial water storage series is determined.

[0059] 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 5 artificial intelligence models, specifically 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.

[0060] For the five artificial intelligence models, the weight parameters of the multi-model weighted average method are derived. Specifically, they include:

[0061] 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:

[0062] For each month, the weight parameters of the 5 AI models satisfy:

[0063]

[0064] in, i Indicates the rate i AI models; Indicates i The weight parameters of the AI ​​model LM Indicates that the number of AI models is 5;

[0065] The weights are calculated using the following formula:

[0066] ;

[0067] in, RMS i No. i The absolute deviation between the terrestrial water storage simulated by an artificial intelligence model and the terrestrial water storage inverted by gravity satellites.

[0068] In step 104, a normalized comprehensive drought index is determined based on the vegetation-drought regression model according to the land drought intensity under the global climate model and the normalized runoff drought intensity under the global climate model.

[0069] As an example, the construction of the vegetation-drought regression model includes:

[0070] Establish vegetation growing season based on satellite-derived vegetation data;

[0071] The drought intensity is estimated based on the land water storage data inverted from gravity satellites;

[0072] Based on the monthly runoff depth data of ERA5, the standardized runoff drought intensity was derived;

[0073] According to the drought intensity, the standardized runoff drought intensity, and based on the Thiessen polygon method, a vegetation-land-atmosphere drought regression model in the growing season is constructed.

[0074] The above process of determining the vegetation growing season may include:

[0075] A. Using the hourly meteorological and hydrological observation data of the fifth generation atmospheric reanalysis data set (ERA5) collected by 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 are calculated in turn, and then the monthly scale LAI and solar induced chlorophyll fluorescence data (SIFD) after regional spatial averaging are calculated using the Thiessen polygon method;

[0076] The mean of LAI and SIFD 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.

[0077] 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.

[0078] In actual operation, the above Normally, standardization is required. The process of obtaining the standardized SIF series is:

[0079] ;

[0080] Where: Indicates j Months of standardized SIF data, represents the original SIF data of the jth month; 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.

[0081] The process of calculating drought intensity in the previous article can be expressed by the following formula:

[0082]

[0083] 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.

[0084] Regarding the terrestrial water storage data inverted by gravity satellites, 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 in the United States, the Center for Space Research at University of Texas, Austin (CSR) and the Goddard Space Flight Center (GSFC) of NASA, etc., which publish the global gravity field output data on a monthly scale. The present invention uses the latest sixth-generation (RL06) products released by JPL, CSR and GSFC at the same time. 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 possible uncertainties caused by different data products, the three 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 a monthly dataset of terrestrial water storage from 2002 to the present.

[0085] The standardized runoff drought intensity derived in the previous article can be expressed by the following formula:

[0086] ;

[0087] Where: Indicates j Months of normalized runoff drought intensity, ROF j Indicates j Months of runoff depth; Respectively m The mean monthly runoff depth, No. m The standard deviation of monthly runoff depth for the month, m =1,2…,12, according to j Determine the calendar month in which the month falls.

[0088] The vegetation-land-atmosphere drought regression model for the growing season mentioned above is:

[0089] ;

[0090] in, is the VEG anomaly of the mth month relative to the entire growing season; For the mMonthly TWSA-DSI index; For the m Monthly RRDD index; , are model parameters, and the least square method is used to solve these two parameters.

[0091] The normalized comprehensive drought index determined in the previous article can be expressed by the following formula:

[0092] ;

[0093] Where: DI is the normalized drought index; RRDD and TWSA-DSI are the normalized runoff drought intensity and land drought intensity simulated by the global climate model, respectively.

[0094] In step 106, based on the disease-high temperature relationship model during the vegetation growing season, a normalized high temperature index is determined according to the daily average wet-bulb temperature of each grid point and the daily average pathogenic high temperature stress index under the global climate model.

[0095] As an example, the construction of the disease-high temperature relationship model during the vegetation growing season includes:

[0096] According to the hourly 2m air temperature and dew point temperature provided by the ERA5 dataset, the hourly wet bulb temperature of each grid point is calculated;

[0097] For example, .

[0098] Where: T w is the estimated wet bulb temperature, T 2m is the air temperature at 2 m, atan is the inverse tangent function; RH is the relative humidity, which is derived using the Clausius-Clapeyron thermodynamic equation, specifically: .

[0099] According to the hourly wet-bulb temperature and relative humidity, the hourly pathogenic high temperature stress index of each grid point is calculated;

[0100] For example, ;

[0101] Where: T h To derive the pathogenic high temperature stress index.

[0102] Based on the hourly pathogenic high temperature stress index, the monthly-scale disease and death case data occurring in the region during the heat wave-drought composite event prediction can also be used to establish a disease-heat wave association model for the vegetation growing season.

[0103] For example, ;

[0104] middle: Indicates the study area y The total number of illnesses and deaths during the annual flood season, DD y Indicates y Number of days in the vegetation growing season per year; Indicates y The vegetation growth season of the year dd The pathogenic high temperature stress index of the day, Indicates y The vegetation growth season of the year dd daily wet-bulb temperature; are the parameters of the disease-heat wave association model.

[0105] The least squares method is used to solve the disease-heat wave association model for each grid point in the vegetation growing season, and the parameters are obtained. It is worth noting that in order to fully consider the spatial heterogeneity of heat waves, the present invention The parameters may be inconsistent, so a regression model is constructed for each grid point.

[0106] In this step, the normalized high temperature index is determined according to the daily average wet-bulb temperature and the daily average pathogenic high temperature stress index of each grid point under the global climate model, which can be expressed by the following formula:

[0107] ;

[0108] Where: HI is the normalized high temperature index; are the parameters of the disease-heat wave association model, and the parameter values ​​at each grid point may be different.

[0109] In step 108, the occurrence of a heat wave-drought composite event is predicted based on the normalized comprehensive drought index and the normalized high temperature index.

[0110] As an example, the forecast of the occurrence of heatwave-drought complex events includes:

[0111] Determining the intensity of the heat wave-drought composite event according to the normalized comprehensive drought index and the normalized high temperature index;

[0112] For example, the intensity of a heatwave-drought composite event can be expressed by the following formula:

[0113] ;

[0114] Where: represents the duration of the heat wave-drought composite event, in days; HI d The heat wave-drought complex event dThe normalized high temperature index for the day; DI d The heat wave-drought complex event d Daily Normalized Composite Drought Index; represents the intensity of the heatwave-drought composite event; They represent the 75% and 25% quantiles of the normalized high temperature index, respectively.

[0115] The relative change status of the heat wave-drought compound event at each grid point in the global climate model is obtained from the intensity, and the uncertainty of the occurrence of the heat wave-drought compound event is characterized by the standard deviation of the relative change status.

[0116] Here, the relative change in intensity is expressed by the following formula,

[0117] ;

[0118] Where: is the relative change in the intensity of the heat wave-drought composite event at the g-th grid point under the m-th global climate model; is the mean intensity of heat wave-drought composite events in the future period at the g-th grid point under the m-th global climate model; is the mean intensity of heat wave-drought composite events in the historical period at the g-th grid point under the m-th global climate model.

[0119] Please refer to Figure 2 , which shows a block diagram of the heat wave-drought combined event prediction device provided by the present application. The device 200 includes:

[0120] A first determination module 202 is used to determine the land drought intensity under the global climate model according to the land water storage series under the global climate model, wherein the land water storage series is determined at least according to the saturated water vapor pressure deficit under the global climate model;

[0121] A second determination module 204 is used to determine a normalized comprehensive drought index based on a vegetation-drought regression model according to the land drought intensity under the global climate model and the normalized runoff drought intensity under the global climate model;

[0122] The third determination module 206 is used to determine the normalized high temperature index based on the disease-high temperature relationship model in the vegetation growing season and the daily average wet-bulb temperature and the daily average pathogenic high temperature stress index of each grid point in the global climate model;

[0123] The fourth determination module 208 is used to predict the occurrence of a heat wave-drought compound event according to the normalized comprehensive drought index and the normalized high temperature index.

[0124] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 be executed, or the hardware and software modules in the decoding processor can be combined and executed. 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.

[0129] 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.

[0130] 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.

[0131] 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 heat wave-drought composite event prediction method, characterized in that: include: Determine the intensity of land drought in the global climate model based on the land water storage series in the global climate model, the land water storage series is determined based on at least the saturated water vapor pressure deficit in the global climate model; Based on the vegetation-drought regression model, a normalized comprehensive drought index is determined according to the land drought intensity under the global climate model and the normalized runoff drought intensity under the global climate model; Based on the disease-high temperature relationship model during the vegetation growing season, the normalized high temperature index was determined according to the daily average wet-bulb temperature and daily average pathogenic high temperature stress index of each grid point in the global climate model; It is used to predict the occurrence of heat wave-drought compound events according to the normalized comprehensive drought index and the normalized high temperature index.

2. The method according to claim 1, characterized in that Determination of the terrestrial water storage series under the global climate model, including: The terrestrial water storage series is determined based on the ENSO index, the saturated vapor pressure deficit in the global climate model, the monthly average 2m temperature, monthly precipitation, monthly snowfall, monthly runoff depth and monthly shortwave radiation intensity output by the global climate model.

3. The method according to claim 2, characterized in that The saturated water vapor pressure deficit under the global climate model is determined according to the monthly average 2m air temperature and the relative humidity under the global climate model, and based on the Clausius-Clapeyron thermodynamic equation.

4. The method according to claim 2, characterized in that: Determine the terrestrial water storage series, specifically: Based on multiple artificial intelligence models and the corresponding weights of each artificial intelligence model, the terrestrial water storage series is determined.

5. The method according to claim 1, characterized in that The construction of the vegetation-drought regression model includes: Establish vegetation growing season based on satellite-derived vegetation data; Based on the land water storage data inverted from gravity satellites, the land drought intensity is estimated; Based on the monthly runoff depth data of ERA5, the standardized runoff drought intensity was derived; According to the land drought intensity and the standardized runoff drought intensity, and based on the Thiessen polygon method, a vegetation-drought regression model in the growing season is constructed.

6. The method according to claim 1, characterized in that The construction of the disease-high temperature relationship model during the vegetation growing season includes: According to the hourly 2m air temperature and dew point temperature provided by the ERA5 dataset, the hourly wet bulb temperature of each grid point is calculated; According to the hourly wet-bulb temperature and relative humidity, the hourly pathogenic high temperature stress index of each grid point is calculated; Based on the hourly pathogenic high temperature stress index, a disease-high temperature relationship model for the vegetation growing season is established.

7. The method according to claim 1, characterized in that Predict the occurrence of combined heatwave-drought events, including: Determining the intensity of the heat wave-drought composite event according to the normalized comprehensive drought index and the normalized high temperature index; The relative change status of the heat wave-drought compound event at each grid point in the global climate model is obtained from the intensity, and the uncertainty of the occurrence of the heat wave-drought compound event is represented by the standard deviation of the relative change status.

8. A heat wave-drought combined event prediction device, characterized in that: include: A first determination module is used to determine the land drought intensity under the global climate model according to the land water storage series under the global climate model, wherein the land water storage series is determined at least according to the saturated water vapor pressure deficit under the global climate model; A second determination module is used to determine a normalized comprehensive drought index based on a vegetation-drought regression model according to the land drought intensity under the global climate model and the standardized runoff drought intensity under the global climate model; The third determination module is used to determine the normalized high temperature index based on the disease-high temperature relationship model during the vegetation growing season and the daily average wet-bulb temperature and daily average pathogenic high temperature stress index of each grid point under the global climate model; The fourth determination module is used to predict the occurrence of a heat wave-drought composite event based on the normalized comprehensive drought index and the normalized high temperature index.

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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