Method and device for predicting drought-flood sudden change events

Abstract

The present application discloses a method and device for predicting drought-flood abrupt transition events. An emergent constraint model is generated through the change trend of the average monthly precipitation in the growing season of the historical period output by each global climate model and the change trend of the normalized water surplus and deficit index in the future period output by the global climate model. Based on the emergent constraint model, the change trend of the normalized water surplus and deficit index in the future period after correction is determined by the change trend of the monthly precipitation in the growing season of the historical period of the ERA5 data set, so as to improve the prediction accuracy of the evolution of the evolution through the evolution of drought-flood abrupt transition 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 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 floods and droughts 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 water and drought disasters 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. Continuous high temperature drought causes soil dehydration and loosening or agglomeration and hardening, and a large accumulation of atmospheric energy. Severe convective weather and concentrated short-term heavy rainfall are very likely to occur with a slight disturbance, which is very likely to cause sudden flash floods and induce extreme disasters with multiple disaster-causing factors superimposed. In recent years, when there is no precipitation or less precipitation in the early stage and drought has occurred, it is easy to follow with high-intensity precipitation in a short period of time and form floods. This phenomenon is called drought-flood abrupt transition. Due to the rapid transition of natural states, short time and high rainfall intensity, it is easy to cause flash floods, steep rise of river water, river water intrusion and other phenomena. The drought-flood abrupt transition poses a great challenge to the prevention of water and drought disasters, and early prevention is required. However, it is still unclear how to monitor and predict future drought-flood abrupt transition disasters.

[0004] At the regional and global scales, drought and flood are 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 flood and drought disasters on vegetation carbon budget and found that extreme flood and drought disasters cause a significant decrease in total primary productivity and ecosystem net productivity.

[0005] It is not difficult to find that it is difficult to accurately predict the evolution trend of drought-flood sudden changes using relevant technologies. Summary of the invention

[0006] In view of this, the heat wave-drought composite event prediction method and device of the present application can accurately predict the evolution trend of drought-flood sudden transition events.

[0007] In a first aspect, the present application provides a method for predicting drought-flood sudden transition events, comprising:

[0008] Determine spatially averaged land drought intensity based on land water storage series in global climate models;

[0009] Based on the vegetation-water balance correlation model, a normalized water balance index is determined according to the land drought intensity, the spatially averaged atmospheric water balance index under the global climate model, and the runoff water balance index under the global climate model;

[0010] According to the trend of the average monthly precipitation in the growing season of each global climate model in the historical period and the trend of the normalized water surplus and deficit index in the future period output by the global climate model, the emergence constraint model is constructed;

[0011] Based on the emergence constraint model, the changing trend of the normalized water surplus and deficit index in the future period after correction is determined according to the changing trend of the monthly precipitation in the growing season of the historical period of the ERA5 dataset;

[0012] The evolution of drought-flood sudden change events is predicted by taking a weighted average of the change trends of the normalized water surplus and deficit index in the future period after correction corresponding to each global climate model.

[0013] Optionally, the determination of the terrestrial water storage series under the global climate model comprises:

[0014] The terrestrial water storage series is 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.

[0015] Optionally, the terrestrial water storage series is determined as follows:

[0016] Based on multiple artificial intelligence models and the corresponding weights of each artificial intelligence model, the terrestrial water storage series is determined.

[0017] Optionally, the spatially averaged atmospheric moisture surplus and deficit index is determined based on monthly relative humidity data under a global climate model.

[0018] Optionally, the runoff moisture surplus and deficit index is determined based on a monthly runoff series at an outlet section of a watershed predicted to be prone to drought or flood.

[0019] Optionally, the monthly runoff series of the outlet section is determined according to the hydrological and meteorological data output by the global climate model under the SSP5-85 shared socioeconomic pathway and based on the global hydrodynamic model CamaFlood.

[0020] Optionally, the construction of the vegetation-water surplus and deficit association model includes:

[0021] Establish vegetation growing season based on satellite-derived vegetation data;

[0022] 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 drought-flood abrupt transition prediction basin, the atmospheric moisture surplus and deficit index is determined;

[0023] The drought intensity is estimated based on the land water storage data inverted from gravity satellites;

[0024] Based on the monthly runoff series of the hydrological station at the outlet section of the basin, the runoff surplus and deficit index is derived;

[0025] According to the atmospheric water surplus and deficit index, drought intensity, runoff surplus and deficit index, and based on the Thiessen polygon method, a vegetation-land-atmosphere drought regression model in the growing season is constructed.

[0026] In a second aspect, the present application provides a drought-flood rapid transition event prediction device, comprising:

[0027] A first determination module is used to determine the spatially averaged land drought intensity based on the land water storage series in the global climate model;

[0028] A second determination module is used to determine a normalized water surplus and deficit index based on a vegetation-water surplus and deficit correlation model according to the land drought intensity, the spatially averaged atmospheric water surplus and deficit index under a global climate model, and the runoff water surplus and deficit index under a global climate model;

[0029] A construction module is used to construct an emergence constraint model according to the trend of the change of the average monthly precipitation in the growing season of the watershed in the historical period output by each global climate model and the trend of the change of the normalized water surplus and deficit index in the future period output by the global climate model;

[0030] The third determination module is used to determine the change trend of the normalized water surplus and deficit index in the future period after correction based on the emergence constraint model and the change trend of the monthly precipitation in the growing season of the historical period of the ERA5 data set;

[0031] The prediction module is used to predict the evolution of drought-flood sudden change events by weighted averaging the change trends of the normalized water surplus and deficit index in the future period after correction corresponding to each global climate model.

[0032] 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.

[0033] 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;

[0034] The memory is used to store programs;

[0035] The processor is used to execute the program in the memory, so that the execution device executes the above method.

[0036] In the method disclosed in the present application, an emergence constraint model is established through the changing trend of the average monthly precipitation in the growing season of the historical period output by each global climate model and the changing trend of the normalized water surplus and deficit index in the future period output by the global climate model, and based on the emergence constraint model, the changing trend of the normalized water surplus and deficit index in the future period after correction is determined by the changing trend of the monthly precipitation in the growing season of the historical period of the ERA5 data set, so as to improve the prediction accuracy of the evolution status through the evolution status of the sudden transition from drought to flood events. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] 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.

[0038] Figure 1 An operational flow chart of a method for predicting drought-flood transition events provided by an exemplary embodiment is shown.

[0039] Figure 2 A block diagram of a drought-flood sudden change event prediction device provided by an exemplary embodiment is shown;

[0040] Figure 3 A block diagram of the execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0041] 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.

[0042] 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.

[0043] Please refer to Figure 1 , Figure 1 A flowchart of a method for predicting drought-flood transition events provided by an exemplary embodiment is shown. The method is implemented through steps 102-110.

[0044] In step 102, the spatially averaged land drought intensity is determined based on the land water storage series in the global climate model.

[0045] As an example, the terrestrial water storage series can be 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] Specifically, based on multiple artificial intelligence models and the weights corresponding to each artificial intelligence model, the terrestrial water storage series is determined.

[0052] 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.

[0053] For the five artificial intelligence models, the weight parameters of the multi-model weighted average method are derived. Specifically, they include:

[0054] 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:

[0055] For each month, the weight parameters of the 5 AI models satisfy:

[0056]

[0057] 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;

[0058] The weights are calculated using the following formula:

[0059] ;

[0060] 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.

[0061] In step 104, based on the vegetation-water balance correlation model, a normalized water balance index is determined according to the land drought intensity, the spatially averaged atmospheric water balance index in the global climate model, and the runoff water balance index in the global climate model.

[0062] As an example, the construction of the vegetation-water deficit correlation model includes:

[0063] Establish vegetation growing season based on satellite-derived vegetation data;

[0064] 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 drought-flood abrupt transition prediction basin, the atmospheric moisture surplus and deficit index is determined;

[0065] The drought intensity is estimated based on the land water storage data inverted from gravity satellites;

[0066] Based on the monthly runoff series of the hydrological station at the outlet section of the basin, the runoff surplus and deficit index is derived;

[0067] According to the atmospheric water surplus and deficit index, drought intensity, runoff surplus and deficit index, and based on the Thiessen polygon method, a vegetation-land-atmosphere drought regression model in the growing season is constructed.

[0068] The above process of determining the vegetation growing season may include:

[0069] 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.

[0070] 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.

[0071] 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.

[0072] In actual operation, the above Normally, standardization is required. The process of obtaining the standardized SIF series is:

[0073] ;

[0074] Where: Indicates j Months of standardized SIF data, SIF j 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.

[0075] The monthly average NDVI (Normalized Difference Vegetation Index) dataset for drought-flood rapid transition prediction basins is obtained by averaging the data of the first 15 days and the last 15 days of each month from satellite inversion data, and then reconstructing the monthly average NDVI data using the maximum synthesis method.

[0076] The process of calculating drought intensity in the previous article can be expressed by the following formula:

[0077] ;

[0078] 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.

[0079] 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.

[0080] The process of deriving the atmospheric moisture surplus and deficit index in the previous article can be expressed by the following formula:

[0081] ;

[0082] Where: Indicates j Monthly atmospheric moisture surplus and deficit index, RH j Indicates j RH index of the month; and Respectively m The mean and standard deviation of monthly RH, m =1,2…,12, according to j Determine the calendar month in which the month falls.

[0083] Here, the RH index, i.e., relative humidity, can be calculated using the Clausius-Clapeyron thermodynamic equation. The Clausius-Clapeyron thermodynamic equation is defined as follows:

[0084] ;

[0085] 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. Specifically, the 2-meter air temperature and dew point temperature of the meteorological and hydrological observation samples of each grid point in the drought-flood transition prediction basin are substituted into the Clausius-Clapeyron thermodynamic equation as input variables, and the relative humidity (RH) of each grid point is calculated, which is: , where T dew is the dew point temperature, T 2m The air temperature is 2m.

[0086] The process of deriving the runoff profit and loss index in the previous article can be expressed by the following formula:

[0087] ;

[0088] Where: Indicates j Monthly runoff profit and loss index, Indicates j Monthly runoff series for the month; and Respectively m The mean and standard deviation of monthly runoff, m=1,2…,12, according to j Determine the calendar month in which the month falls.

[0089] The vegetation-land-atmosphere drought regression model in the growing season can be represented by the following formula:

[0090] ;

[0091] 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 Monthly RHDD index; For the m The RunoffD index of the month; α, β, and γ are model parameters, and the least squares method is used to solve these three parameters.

[0092] As an example, the spatially averaged atmospheric moisture surplus and deficit index is determined based on monthly relative humidity data in a global climate model.

[0093] Here, the method of calculating the monthly relative humidity data under the global climate model can refer to the method of "determining the atmospheric moisture surplus and deficit index by calculating the relative humidity of each grid point based on the 2-meter air temperature and dew point temperature of the meteorological and hydrological observation samples of each grid point in the drought-flood sudden transition prediction basin during the construction of the vegetation-water surplus and deficit correlation model", and will not be repeated here.

[0094] As an example, the runoff water surplus and deficit index is determined based on the monthly runoff series of the outlet section of the drought-flood rapid transition prediction basin.

[0095] As a reference implementation method, the calculation method of the monthly runoff series of the outlet section is based on the model data of daily average temperature, daily average relative humidity, daily precipitation, daily snowfall, daily runoff depth and daily shortwave radiation intensity in the historical period (1940-2014) and future period (2030-2100) of 36 global climate models under the SSP5-85 shared socioeconomic pathway, embedded with the river network data of the drought-flood rapid transition prediction basin, and driven by the global hydrodynamic model CamaFlood to simulate the daily runoff series of the outlet section of the drought-flood rapid transition prediction basin in the historical period and future period, and obtain the monthly runoff series of the outlet section through time scale conversion.

[0096] After calculating the normalized water surplus and deficit index, the possibility and intensity of drought-flood sudden transition events can be predicted based on the normalized water surplus and deficit index.

[0097] Specifically, based on the normalized water surplus and deficit index DWI of the historical period, the thresholds corresponding to the 20% quantile and the 80% quantile of the DWI are calculated, which are called the drought threshold and the flood threshold, respectively; the months below the drought threshold are called drought months, and the months above the flood threshold are called flood months; if there is one drought month and one flood month in two adjacent months, it means that a sudden transition from drought to flood has occurred.

[0098] For a certain dry month, if multiple consecutive months with this month are flood months, the month with the highest DWI index is taken as the flood month; for a certain flood month, if multiple consecutive months with this month are dry months, the month with the lowest DWI index is taken as the dry month; therefore, the drought-flood rapid transition event defined in the present invention includes two scenarios: from flood to drought and from drought to flood, and the duration of the drought-flood rapid transition event is 2 months.

[0099] Furthermore, the intensity measurement index of drought-flood sudden change events is proposed, as follows:

[0100] ;

[0101] Where: CDHW represents the intensity of drought-flood sudden change events; DWI W and DWI D They respectively characterize the DWI intensity in the flood month and the dry month in the drought-flood abrupt transition event; The standard deviation of the DWI index in the basin is used to predict the sudden transition from drought to flood.

[0102] Furthermore, in each global climate model, for the drought-flood abrupt transition basins predicted, the mean values ​​of the intensity of drought-flood abrupt transition events in the historical period and the future period are calculated, and their relative changes are calculated:

[0103] ;

[0104] Where: is the relative change in the intensity of drought-flood abrupt transition events under the mth global climate model; is the mean intensity of drought-flood abrupt transition events in the future period under the mth global climate model; is the mean intensity of drought-flood abrupt transition events in the historical period under the mth global climate model.

[0105] Furthermore, for each grid point in the drought-flood transition event prediction area, the standard deviation of the relative change values ​​predicted by the five global climate models was calculated, and this value was used to characterize the uncertainty of the prediction of future drought-flood transition events.

[0106] In step 106, an emergence constraint model is constructed based on the changing trend of the average monthly precipitation in the growing season of the basin in the historical period (e.g., 1940-2014) output by each global climate model and the changing trend of the normalized water surplus and deficit index in the future period (e.g., 2030-2100) output by the global climate model.

[0107] Specifically, the emergence constraint model can be represented by the following formula:

[0108] ;

[0109] Where: They respectively represent the changing trend of the DWI index in the future period and the changing trend of the average monthly precipitation in the basin during the growing season in the historical period; a and b represent model parameters; the least squares method is used to solve the parameters a and b of the emergence constraint model.

[0110] In step 108, based on the emergence constraint model, the changing trend of the annual normalized water deficit index in the future period (eg, 1940-2014) after correction is determined according to the changing trend of the monthly precipitation in the growing season of the historical period of the ERA5 dataset.

[0111] ;

[0112] Where: They respectively represent the changing trend of the corrected DWI index in the future period and the changing trend of the monthly precipitation in the growing season of the historical period of the ERA5 dataset.

[0113] In step 110, the evolution of drought-flood sudden change events is predicted by weighted averaging the change trends of the normalized water surplus and deficit index in the future period after correction corresponding to each global climate model.

[0114] Here, the weights for weighted averaging may be arbitrarily selected according to actual needs.

[0115] Please refer to Figure 2 , which shows a block diagram of the drought-flood transition event prediction device provided by the present application. The device 200 includes:

[0116] A first determination module 202 is used to determine the spatial average land drought intensity according to the land water storage series under the global climate model;

[0117] A second determination module 204 is used to determine a normalized water deficit index based on a vegetation-water deficit correlation model, according to the land drought intensity, the spatially averaged atmospheric water deficit index under a global climate model, and the runoff water deficit index under a global climate model;

[0118] A construction module 206 is used to construct an emergence constraint model according to the change trend of the average monthly precipitation in the growing season of the watershed in the historical period output by each global climate model and the change trend of the normalized water surplus and deficit index in the future period output by the global climate model;

[0119] The third determination module 208 is used to determine the change trend of the normalized water surplus and deficit index in the future period after correction based on the emergence constraint model and the change trend of the monthly precipitation in the growing season of the historical period of the ERA5 data set;

[0120] The prediction module 210 is used to predict the evolution of drought-flood sudden change events by weighted averaging the change trends of the normalized water surplus and deficit index in the future period after correction corresponding to each global climate model.

[0121] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 1The 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.

[0128] 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 method for predicting drought-flood sudden change events, characterized in that: include: Determine spatially averaged land drought intensity based on land water storage series in global climate models; Based on the vegetation-water balance correlation model, a normalized water balance index is determined according to the land drought intensity, the spatially averaged atmospheric water balance index under the global climate model, and the runoff water balance index under the global climate model; According to the changing trend of the average monthly precipitation in the growing season of each global climate model in the historical period and the changing trend of the normalized water surplus and deficit index in the future period output by the global climate model, an emergence constraint model is constructed; Based on the emergence constraint model, the changing trend of the normalized water surplus and deficit index in the future period after correction is determined according to the changing trend of the monthly precipitation in the growing season of the historical period of the ERA5 dataset; The evolution of drought-flood abrupt transition events is predicted by weighted averaging the change trends of the normalized water surplus and deficit index in the future period after correction corresponding to each global climate model; The construction of the vegetation-water surplus and deficit correlation model includes: Establish vegetation growing season based on satellite-derived vegetation data; 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 drought-flood abrupt transition prediction basin, the atmospheric moisture surplus and deficit index is determined; The drought intensity is estimated based on the land water storage data inverted from gravity satellites; Based on the monthly runoff series of the hydrological station at the outlet section of the basin, the runoff surplus and deficit index is derived; According to the atmospheric water surplus and deficit index, drought intensity, runoff surplus and deficit index, and based on the Thiessen polygon method, a vegetation-land-atmosphere drought regression model in the growing season is constructed.

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 and 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 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.

4. The method according to claim 1, characterized in that: The spatially averaged atmospheric moisture surplus and deficit index is determined based on monthly relative humidity data from a global climate model.

5. The method according to claim 1, characterized in that The runoff moisture surplus and deficit index is determined based on the monthly runoff series of the outlet section of the basin where drought-flood sudden change is predicted.

6. The method according to claim 5, characterized in that The monthly runoff series of the outlet section is determined according to the hydrological and meteorological data output by the global climate model under the SSP5-85 shared socioeconomic pathway and based on the global hydrodynamic model CamaFlood.

7. A drought-flood sudden change event prediction device, characterized in that: include: A first determination module is used to determine the spatially averaged land drought intensity based on the land water storage series in the global climate model; A second determination module is used to determine a normalized water surplus and deficit index based on a vegetation-water surplus and deficit correlation model according to the land drought intensity, the spatially averaged atmospheric water surplus and deficit index under a global climate model, and the runoff water surplus and deficit index under a global climate model; A construction module is used to construct an emergence constraint model according to the change trend of the average monthly precipitation in the growing season of the watershed in the historical period output by each global climate model and the change trend of the normalized water surplus and deficit index in the future period output by the global climate model; The third determination module is used to determine the change trend of the normalized water surplus and deficit index in the future period after correction based on the emergence constraint model and the change trend of the monthly precipitation in the growing season of the historical period of the ERA5 data set; A prediction module, for predicting the evolution of drought-flood abrupt transition events by weighted averaging the change trends of the normalized water surplus and deficit index in the future period after correction corresponding to each global climate model; The construction of the vegetation-water surplus and deficit correlation model in the second determination module includes: Establish vegetation growing season based on satellite-derived vegetation data; 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 drought-flood abrupt transition prediction basin, the atmospheric moisture surplus and deficit index is determined; The drought intensity is estimated based on the land water storage data inverted from gravity satellites; Based on the monthly runoff series of the hydrological station at the outlet section of the basin, the runoff surplus and deficit index is derived; According to the atmospheric water surplus and deficit index, drought intensity, runoff surplus and deficit index, and based on the Thiessen polygon method, a vegetation-land-atmosphere drought regression model in the growing season is constructed.

8. 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 6.

9. 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 6.

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