A method and system for analyzing response of soil drought-flood abrupt change to meteorological drought-flood abrupt change

Abstract

The application discloses a kind of soil drought-flood sudden change response analysis method and system to meteorological drought-flood sudden change, solve the technical problem that the analysis result output by existing drought-flood sudden change analysis method is difficult to accurately reflect the coupling law of both, cannot provide high-quality scientific basis for disaster prevention and mitigation.Method includes obtaining the original precipitation evapotranspiration index data and multilayer depth soil humidity original data of each month of multiple spatial grids in research area, obtains monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil humidity index after pre-processing;According to event identification and merging rule, identify multiple target drought-flood sudden change events of each spatial grid, obtain drought-flood sudden change, flood-drought sudden change pairing events by pairing;Extract the multiple meteorological soil characteristic variable groups corresponding to two types of pairing events, based on these variable groups, carry out response analysis on the research area, and finally output the target response analysis result corresponding to the research area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrology and water resources, and particularly relates to a method and system for analyzing response of soil drought-flood abrupt change to meteorological drought-flood abrupt change. BACKGROUND

[0002] Drought-flood abrupt change is an extreme compound disaster in which a basin changes from drought to flood or from flood to drought in a short period of time. Influenced by global climate change, the frequency and intensity of drought-flood abrupt change continue to rise, which has a significant impact on agricultural production (crop damage and food reduction), water resources management (runoff fluctuation and reservoir scheduling difficulty), and ecological environment (vegetation damage and ecological imbalance).

[0003] Identification and analysis of drought-flood abrupt change is the key to improving the ability of basin water resources regulation and disaster prevention and mitigation. It can make up for the shortcomings of traditional monitoring methods, accurately capture the spatio-temporal characteristics and propagation path of the event, provide scientific basis for reservoir scheduling and irrigation planning, and help target optimization of disaster prevention engineering layout to effectively reduce disaster losses.

[0004] Existing drought-flood abrupt change analysis methods mainly focus on the propagation mechanism of different types of drought events, and these methods mostly rely on traditional linear correlation or empirical statistical ideas. However, there is a significant nonlinear dependence relationship and time lag effect between meteorological drought-flood and soil drought-flood. Traditional methods are difficult to accurately depict this complex response mechanism, resulting in analysis results that cannot accurately reflect the coupling law of the two, and cannot provide high-quality scientific basis for disaster prevention and mitigation. SUMMARY

[0005] The present application provides a method and system for analyzing response of soil drought-flood abrupt change to meteorological drought-flood abrupt change, which solves the technical problem that the analysis results output by existing drought-flood abrupt change analysis methods cannot accurately reflect the coupling law of the two, and cannot provide high-quality scientific basis for disaster prevention and mitigation.

[0006] The present application provides a method for analyzing response of soil drought-flood abrupt change to meteorological drought-flood abrupt change, which includes:

[0007] Obtaining original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of a plurality of spatial grids in a study area in each month;

[0008] Preprocessing the original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of each spatial grid in each month, and outputting monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month;

[0009] The event identification and merging rule is used to identify drought-flood rapid change events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month, and a plurality of target drought-flood rapid change events corresponding to each spatial grid are output.

[0010] The plurality of target drought-flood rapid change events corresponding to each spatial grid are paired, and drought-flood transition pairing events and flood-drought transition pairing events of each spatial grid are output.

[0011] The plurality of meteorological soil characteristic variable groups corresponding to the drought-flood transition pairing events and the plurality of meteorological soil characteristic variable groups corresponding to the flood-drought transition pairing events of each spatial grid are extracted, and a response analysis of the research region is carried out based on the corresponding plurality of meteorological soil characteristic variable groups of each spatial grid, and a target response analysis result corresponding to the research region is output.

[0012] Optionally, the preprocessing of the original precipitation evapotranspiration index data and the multi-layer depth soil moisture original data of each spatial grid in each month is carried out, and the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month are output, including:

[0013] The original precipitation evapotranspiration index data and the multi-layer depth soil moisture original data of each spatial grid in each month are sequentially standardized and time-scaled, and the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized multi-layer depth soil moisture data of each spatial grid in each month are output.

[0014] The monthly scale standardized multi-layer depth soil moisture data of each spatial grid in each month is integrated to obtain a monthly scale standardized comprehensive soil moisture value of each spatial grid in each month.

[0015] Based on the monthly scale standardized comprehensive soil moisture value of each spatial grid in each month, a monthly scale standardized soil moisture index of each spatial grid in each month is calculated.

[0016] Optionally, the event identification and merging rule includes a drought-flood event identification rule, a drought-flood event merging rule, and a drought-flood rapid change event identification rule; the event identification and merging rule is used to identify drought-flood rapid change events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month, and a plurality of target drought-flood rapid change events corresponding to each spatial grid are output, including:

[0017] The drought and flood event identification rules are adopted to identify drought and flood events of each spatial grid according to the monthly scale standardized soil moisture index and the monthly scale standardized precipitation evapotranspiration index data of each spatial grid in each month, and initial drought and flood events of each spatial grid in multiple time periods are output;

[0018] The drought and flood event merging rules are adopted to merge the initial drought and flood events of each spatial grid in multiple time periods, and multiple merged drought and flood events corresponding to each spatial grid are output;

[0019] The drought and flood event identification rules are adopted to identify drought and flood events of each spatial grid according to the monthly scale standardized soil moisture index and the monthly scale standardized precipitation evapotranspiration index data of each spatial grid in each month, and initial drought and flood events of each spatial grid in multiple time periods are output;

[0020] Optionally, the target drought and flood rapid change events include meteorological drought-to-flood events, soil drought-to-flood events, meteorological flood-to-drought events and soil flood-to-drought events; the multiple target drought and flood rapid change events corresponding to each spatial grid are paired, and drought-to-flood paired events and flood-to-drought paired events of each spatial grid are output, including:

[0021] The drought-to-flood events of each meteorological drought-to-flood event and each soil drought-to-flood event of each spatial grid are paired to obtain the drought-to-flood paired events of each spatial grid;

[0022] The flood-to-drought events of each meteorological flood-to-drought event and each soil flood-to-drought event of each spatial grid are paired to obtain the flood-to-drought paired events of each spatial grid.

[0023] Optionally, the multiple meteorological and soil characteristic variable groups corresponding to the drought-to-flood paired events and the multiple meteorological and soil characteristic variable groups corresponding to the flood-to-drought paired events of each spatial grid are extracted, and response analysis is performed on the research area based on the corresponding multiple meteorological and soil characteristic variable groups of each spatial grid, and target response analysis results corresponding to the research area are output, including:

[0024] The multiple meteorological and soil characteristic variable groups corresponding to the drought-to-flood paired events and the multiple meteorological and soil characteristic variable groups corresponding to the flood-to-drought paired events of each spatial grid are extracted;

[0025] The correlation degree of each meteorological and soil characteristic variable group corresponding to each spatial grid is calculated;

[0026] Any meteorological and soil characteristic variable group corresponding to a correlation degree greater than a correlation degree threshold value is taken as a target meteorological and soil characteristic variable group;

[0027] The marginal distribution of each target meteorological and soil characteristic variable group corresponding to each spatial grid is fitted to obtain an optimal marginal distribution corresponding to each target meteorological and soil characteristic variable group;

[0028] construct a plurality of joint distribution Copula models corresponding to each of the target meteorological soil characteristic variable groups based on the optimal marginal distribution corresponding to each of the target meteorological soil characteristic variable groups;

[0029] select an optimal Copula model corresponding to each of the target meteorological soil characteristic variable groups in each of the plurality of joint distribution Copula models corresponding to each of the target meteorological soil characteristic variable groups;

[0030] perform probability calculation on each of the target meteorological soil characteristic variable groups by using each of the optimal Copula models to output conditional probability and interval conditional probability corresponding to each of the target meteorological soil characteristic variable groups;

[0031] perform significance analysis and sensitivity analysis on the research area based on the conditional probability and interval conditional probability corresponding to each of the target meteorological soil characteristic variable groups to output significance analysis results and sensitivity analysis results corresponding to the research area.

[0032] Optionally, the system further comprises:

[0033] calculate a soil moisture variation coefficient corresponding to a meteorological drought-flood event in a drought-to-flood pairing event and a soil moisture variation coefficient corresponding to a meteorological flood-to-drought event in a flood-to-drought pairing event for each of the spatial grids;

[0034] perform soil moisture response intensity difference analysis on the research area based on the corresponding soil moisture variation coefficients of each of the spatial grids to obtain soil moisture response intensity difference analysis results corresponding to the research area;

[0035] calculate a composite time sequence curve corresponding to a meteorological drought-to-flood event in a drought-to-flood pairing event and a composite time sequence curve corresponding to a meteorological flood-to-drought event in a flood-to-drought pairing event for each of the spatial grids;

[0036] perform soil moisture response difference analysis on the research area based on the corresponding composite time sequence curves of each of the spatial grids to output response difference analysis results corresponding to the research area;

[0037] calculate a propagation time corresponding to a meteorological drought-to-flood event in a drought-to-flood pairing event and a propagation time corresponding to a meteorological flood-to-drought event in a flood-to-drought pairing event for each of the spatial grids;

[0038] perform response time lag analysis on the research area based on the corresponding propagation times of each of the spatial grids to output response time lag analysis results corresponding to the research area.

[0039] The second aspect of the present application provides a soil drought-flood rapid change response analysis system for meteorological drought-flood rapid change, which comprises:

[0040] an acquisition module configured to acquire original precipitation evapotranspiration index data and original multi-layer deep soil moisture data of a plurality of spatial grids in a study area in each month;

[0041] a preprocessing module configured to preprocess the original precipitation evapotranspiration index data and the original multi-layer deep soil moisture data of each spatial grid in each month, and output monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month;

[0042] an identification module configured to identify a flood-drought abrupt change event of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month by using an event identification and merging rule, and output a plurality of target flood-drought abrupt change events corresponding to each spatial grid;

[0043] a pairing module configured to pair the plurality of target flood-drought abrupt change events corresponding to each spatial grid, and output a flood-to-drought pairing event and a drought-to-flood pairing event of each spatial grid;

[0044] an analysis module configured to extract a plurality of meteorological and soil characteristic variable groups corresponding to the flood-to-drought pairing event and a plurality of meteorological and soil characteristic variable groups corresponding to the drought-to-flood pairing event of each spatial grid, and perform response analysis on the study area based on the corresponding plurality of meteorological and soil characteristic variable groups of each spatial grid, and output a target response analysis result corresponding to the study area.

[0045] A third aspect of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the soil flood-drought abrupt change response analysis method.

[0046] A fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the soil flood-drought abrupt change response analysis method.

[0047] A fifth aspect of the present application provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the soil flood-drought abrupt change response analysis method.

[0048] From the above technical solutions, the present application has the following advantages:

[0049] The above technical scheme of the present application provides a method for analyzing the response of meteorological drought and flood rapid changes to soil drought and flood rapid changes, obtains original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of a plurality of spatial grids in a research area in each month; pre-processes the original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of each spatial grid in each month, and outputs monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month; identifies drought and flood rapid change events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month by using event identification and merging rules, and outputs a plurality of target drought and flood rapid change events corresponding to each spatial grid; pairs the plurality of target drought and flood rapid change events corresponding to each spatial grid, and outputs drought-to-flood pairing events and flood-to-drought pairing events of each spatial grid; extracts a plurality of meteorological and soil characteristic variable groups corresponding to the drought-to-flood pairing events of each spatial grid and a plurality of meteorological and soil characteristic variable groups corresponding to the flood-to-drought pairing events, and performs response analysis on the research area based on the corresponding plurality of meteorological and soil characteristic variable groups of each spatial grid, and outputs a target response analysis result corresponding to the research area; based on the above scheme, the comparability and reliability of the precipitation evapotranspiration and soil moisture data are ensured through standardized preprocessing, and the drought and flood rapid change events are identified and paired based on the standardized index and the event identification and merging rules, the accurate matching of the meteorological and soil level drought and flood rapid change events is realized, the multi-dimensional coupling relationship between the meteorological drought and flood rapid change and the soil drought and flood rapid change under different spatial grids is fully captured through the extraction of a plurality of meteorological and soil characteristic variable groups and the focus on the response analysis of the research area, and the limitations of the traditional method that only focuses on a single drought event propagation and is difficult to depict complex coupling rules are broken; finally, the target response analysis result of the research area is output, the local rules of each spatial grid are integrated to form a macroscopic understanding, and the coupling rules of the meteorological and soil systems are accurately reflected, thereby providing a high-quality scientific basis with strong pertinence and high reliability for disaster prevention and mitigation decision-making in the research area. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, a brief introduction will be given below to the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without any creative labor.

[0051] Figure 1 A step flow chart of a method for analyzing the response of meteorological drought and flood rapid changes to soil drought and flood rapid changes provided for the first embodiment of the present application;

[0052] Figure 2The soil humidity change rate box plot during the meteorological drought-flood event in different climate regions provided by the first embodiment of the present application;

[0053] Figure 3 The soil humidity change composite time sequence diagram of the meteorological drought-flood event in different climate regions provided by the first embodiment of the present application;

[0054] Figure 4 The proportional column chart of the soil drought-flood event propagation time of the meteorological drought-flood event in different climate regions provided by the first embodiment of the present application;

[0055] Figure 5 The structural block diagram of a soil drought-flood rapid change response analysis system for a meteorological drought-flood rapid change provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0056] The present application provides a soil drought-flood rapid change response analysis method and system for a meteorological drought-flood rapid change, which solves the technical problem that the analysis results output by the existing drought-flood rapid change analysis method cannot accurately reflect the coupling law of the two, and cannot provide high-quality scientific basis for disaster prevention and mitigation.

[0057] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved need to be authorized or agreed by the object when the embodiments of the present application are applied to specific products or technologies, and the collection, use and processing of the related data need to comply with the relevant laws, regulations and standards of the country and region. That is, if the embodiments of the present application involve data related to the object, the data needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant department, and compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the consent of the individual needs to be obtained for all personal information, and the separate consent of the information subject needs to be obtained for sensitive information, and the embodiments also need to be implemented with the authorization and consent of the object.

[0058] Please refer to Figure 1 , Figure 1 The step flow chart of a soil drought-flood rapid change response analysis method for a meteorological drought-flood rapid change provided by the first embodiment of the present application.

[0059] The soil drought-flood rapid change response analysis method provided by the present application comprises:

[0060] Step 101, obtaining original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of multiple spatial grids in the study area in each month.

[0061] The study area refers to a specific geographical range for which the present application conducts analysis related to rapid transition between drought and flood. It is the geographical boundary for all subsequent spatial grid division and data collection and analysis.

[0062] The spatial grid refers to an equal geographical unit formed by dividing the study area according to a predetermined precision (such as latitude and longitude resolution). Through this unit, fine spatial analysis of the study area can be achieved, enabling accurate positioning of specific spatial locations for subsequent identification and response analysis of rapid transition events between drought and flood.

[0063] The original precipitation evapotranspiration index data refers to basic data that reflects the precipitation and evapotranspiration balance state of each spatial grid in the study area in the corresponding month without preprocessing. It is the core basic data for characterizing the drought and flood state at the meteorological level.

[0064] The multi-layer depth soil moisture original data refers to basic data that characterizes the soil moisture level of each spatial grid in the study area in the corresponding month without preprocessing, covering different soil depth levels (such as surface, middle, and deep layers). It is the core basic data for depicting the drought and flood state at the soil level.

[0065] It should be noted that obtaining the original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of multiple spatial grids in the study area in each month provides a basic data source for subsequent calculation of standardized indices for each spatial grid, accurate identification and paired analysis of rapid transition events between drought and flood, ensuring the completeness and relevance of data support in the subsequent analysis process.

[0066] Step 102, preprocessing the original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of each spatial grid in each month, and outputting the monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month.

[0067] It should be noted that the preprocessing of the original data obtained in the previous step eliminates the dimensional differences and removes outliers, outputting the monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month, providing uniform and reliable analysis data for subsequent identification of rapid transition events between drought and flood based on standardized indices.

[0068] Further, step 102 can include the following sub-steps:

[0069] S21, sequentially performing standardization processing and time scale unification on the original precipitation evapotranspiration index data and the original multi-layer depth soil moisture data of each spatial grid in each month, and outputting the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized multi-layer depth soil moisture data of each spatial grid in each month;

[0070] S22, integrating the monthly scale standardized multi-layer depth soil moisture data of each spatial grid in each month to obtain the monthly scale standardized comprehensive soil moisture value of each spatial grid in each month;

[0071] S23, calculating the monthly scale standardized soil moisture index of each spatial grid in each month based on the monthly scale standardized comprehensive soil moisture value of each spatial grid in each month.

[0072] It should be noted that the original precipitation evapotranspiration index data and the original multi-layer depth soil moisture data of each spatial grid in each month are sequentially subjected to standardization processing and time scale unification, wherein the standardization processing can eliminate the dimensional difference and numerical fluctuation interference between different spatial grids and different data types, and the time scale unification ensures that all data are aligned to the "month" level time dimension, and then the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized multi-layer depth soil moisture data of each spatial grid in each month are outputted; on this basis, the monthly scale standardized multi-layer depth soil moisture data of each spatial grid in each month is integrated, the humidity information of different soil depth layers is comprehensively integrated, and the monthly scale standardized comprehensive soil moisture value which can comprehensively reflect the overall soil wetting state of the grid in the corresponding month is obtained; then, based on the monthly scale standardized comprehensive soil moisture value of each spatial grid in each month, the monthly scale standardized soil moisture index of each spatial grid in each month is obtained by combining the pre-designed calculation rule, which provides a unified and accurate soil drought and flood state characterization index for subsequent drought and flood sudden change event identification. The above processing procedure effectively improves the uniformity, reliability and comprehensiveness of meteorological and soil related data by optimizing data quality step by step and integrating multi-dimensional soil information, lays a solid data foundation for subsequent accurate identification of drought and flood sudden change events and analysis of meteorological-soil coupling relationship, and provides higher quality data analysis support for disaster prevention and mitigation.

[0073] The monthly scale standardized soil moisture index (SSMI, Standardized Soil Moisture Index) is a dimensionless drought index, which is used to quantify the deviation of soil moisture data from the long-term average, so as to evaluate the surface dry and wet conditions. It is defined as the standardized ratio of the deviation of the soil moisture value of a grid point in the dth month and the yth year from the long-term average value to the standard deviation, and the calculation formula is as follows:

[0074] ;

[0075] wherein, is the monthly scale standardized soil moisture index of the yth year and dth month; is the monthly scale standardized integrated soil moisture value of the yth year and dth month; and denote the multi-year average value and the standard deviation of the dth month, respectively, and the calculation formula is as follows:

[0076]

[0077] wherein, Y is the number of data years. According to the monthly scale standardized precipitation evapotranspiration index data SPEI (Standardized Precipitation Evapotranspiration Index) and the SSMI value, the dry and wet characteristics are classified, as shown in Table 1:

[0078] Table 1 Classification of dry and wet states based on SPEI and SSMI

[0079]

[0080] Step 103, using event identification and merging rules, identifying drought-flood abrupt change events for each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month, and outputting a plurality of target drought-flood abrupt change events corresponding to each spatial grid.

[0081] The event identification and merging rules include drought-flood event identification rules, drought-flood event merging rules, and drought-flood abrupt change event identification rules. The event identification and merging rules refer to a rule set for determining drought-flood abrupt change events, which is based on the state changes of the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index, identifies and merges continuous time periods that meet the “drought to flood” or “flood to drought” characteristics, to determine the rule system of drought-flood abrupt change events.

[0082] Drought-flood abrupt change events refer to state transition processes of meteorological or soil layers within a spatial grid, from drought state to flood state (drought to flood) or from flood state to drought state (flood to drought), which are event units determined based on state changes of monthly scale standardized indices.

[0083] Target drought-flood abrupt change events refer to drought-flood abrupt change events that meet the research requirements identified from the drought-flood state changes of each spatial grid through the event identification and merging rules, which are basic event objects for subsequent event pairing.

[0084] ​It should be noted that the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid obtained based on the pre-processing are used to determine the dynamic change of drought and flood state of each spatial grid by using the event identification and merging rule, to identify the state transition process from drought to flood or from flood to drought in the grid, and to output a plurality of target drought and flood sudden change events corresponding to each spatial grid, thereby providing a basic event unit for subsequent pairing of the events.

[0085] Further, step 103 can include the following sub-steps:

[0086] S31, identifying drought and flood events according to the monthly scale standardized soil moisture index and the monthly scale standardized precipitation evapotranspiration index data of each spatial grid in each month by using the drought and flood event identification rule, and outputting initial drought and flood events of each spatial grid in multiple time periods;

[0087] S32, merging the initial drought and flood events of each spatial grid in multiple time periods by using the drought and flood event merging rule, and outputting a plurality of merged drought and flood events corresponding to each spatial grid;

[0088] S33, identifying drought and flood sudden change events from the plurality of merged drought and flood events corresponding to each spatial grid by using the drought and flood sudden change event identification rule, and outputting a plurality of target drought and flood sudden change events corresponding to each spatial grid.

[0089] Drought and flood event identification rule: refers to a rule system based on the monthly scale standardized soil moisture index (SSMI) and the monthly scale standardized precipitation evapotranspiration index (SPEI) data, taking ±0.5 as the judgment threshold of flood and drought events, defining a period of more than two consecutive months exceeding the threshold as a flood event or a drought event, and used to split the initial drought and flood events from the time series data.

[0090] Drought and flood event merging rule: refers to a rule for integrating adjacent initial drought and flood events of the same type in each spatial grid, specifically, if two adjacent initial drought and flood events of the same type are separated by 1 month, and the SPEI or SSMI value of the interval month is between 0 and 0.5 or -0.5 and 0, then the two events are merged into one complete event to eliminate event fragmentation interference.

[0091] Drought and flood sudden change event identification rule: refers to a rule for determining the state transition of the merged drought and flood events of each spatial grid, specifically, if adjacent drought and flood events are separated by less than 1 month, then the adjacent events are defined as drought and flood sudden change events, which are used to accurately capture the rapid transition process of drought and flood state.

[0092] It should be noted that, based on the run theory, the SPEI or SSMI threshold values of the flood event and the drought event are respectively set to ±0.5, and the event is defined as a flood or drought event if the threshold value is exceeded for two consecutive months or more; if the interval between two adjacent events of the same kind is 1 month, and the SPEI or SSMI value of the month is between 0 and 0.5 or -0.5 and 0, the two events are combined into one event; if the interval between the drought and flood events is less than 1 month, it is defined as a drought-flood rapid transition event.

[0093] Specifically, based on the monthly scale standardized soil moisture index (SSMI) and the monthly scale standardized precipitation evapotranspiration index (SPEI) data of each spatial grid obtained in the preceding sequence, the drought-flood event identification rule is used to identify the drought-flood event, specifically, ±0.5 is taken as the SPEI or SSMI threshold value of the flood event and the drought event, the corresponding drought-flood state of each month is determined, and the period in which the threshold value is exceeded for two consecutive months or more is defined as a flood event or a drought event, and then the initial drought-flood events of each spatial grid in multiple periods are output; then the drought-flood event merging rule is used to process the multiple initial drought-flood events of each spatial grid, if the interval between two adjacent initial drought-flood events of the same kind is 1 month, and the SPEI or SSMI value of the interval month is between 0 and 0.5 or -0.5 and 0, the two adjacent events of the same kind are combined into one complete event, and the multiple merged drought-flood events corresponding to each spatial grid are output; finally, the drought-flood rapid transition event identification rule is used to determine the state transition of the multiple merged drought-flood events of each spatial grid, if the adjacent drought event and flood event are identified to have an interval of less than 1 month, the adjacent events are defined as a drought-flood rapid transition event, and the multiple target drought-flood rapid transition events corresponding to each spatial grid are output. The process realizes the accurate identification of the drought-flood rapid transition event based on the meteorological (SPEI) and soil (SSMI) dual-dimensional standardized indexes through the clear quantitative threshold value and the refined event determination and merging rule, avoids the one-sidedness of the single index identification, eliminates the interference of the scattered short-period events on the identification result through the merging rule, guarantees the integrity and accuracy of the target drought-flood rapid transition event, and provides a precise event unit for the subsequent in-depth analysis of the meteorological-soil coupling rule.

[0094] Step 104, pairing the multiple target drought-flood rapid transition events corresponding to each spatial grid, and outputting the drought-to-flood paired event and the flood-to-drought paired event of each spatial grid.

[0095] The target drought-flood rapid transition event includes a meteorological drought-flood rapid transition event and a soil drought-flood rapid transition event; the meteorological drought-flood rapid transition event includes a meteorological drought-to-flood event and a meteorological flood-to-drought event; and the soil drought-flood rapid transition event includes a soil drought-to-flood event and a soil flood-to-drought event.

[0096] It should be noted that, based on the pre-sequenced identification of the corresponding multiple target drought-flood abrupt transition events of each spatial grid, the events are classified and paired according to the state transition type, the target drought-flood abrupt transition events that are transformed from drought to flood are classified into one category to form a drought-to-flood paired event, and the target drought-flood abrupt transition events that are transformed from flood to drought are classified into one category to form a flood-to-drought paired event, and the drought-to-flood paired events and the flood-to-drought paired events of each spatial grid are output, which lays a foundation for subsequent extraction of meteorological and soil characteristic variable groups corresponding to the two types of paired events.

[0097] Further, step 104 can include the following sub-steps:

[0098] S41, pairing each meteorological drought-to-flood event and each soil drought-to-flood event of each spatial grid to obtain a drought-to-flood paired event of each spatial grid;

[0099] S42, pairing each meteorological flood-to-drought event and each soil flood-to-drought event of each spatial grid to obtain a flood-to-drought paired event of each spatial grid.

[0100] It should be noted that the pairing process of meteorological drought-flood abrupt transition events and soil drought-flood abrupt transition events is as follows:

[0101] ①On the same spatial grid, the start and end time information of meteorological drought-flood abrupt transition events and soil drought-flood abrupt transition events are extracted respectively, and classified according to the event type (drought-to-flood or flood-to-drought).

[0102] ②Taking a drought-to-flood event as an example, the time difference between the soil flood start time and the meteorological flood start time at the same grid point is calculated If is less than or equal to the duration of the corresponding meteorological event plus 1 month, it is preliminarily determined that there is a corresponding relationship between the meteorological event and the soil event.

[0103] ③Further calculate the difference between the soil drought start time and the meteorological drought start time in the paired event If ≥ 0, it is determined that the pairing is successful; if < 0, it is necessary to further determine whether the drought event in the soil drought-to-flood event occurs during a meteorological drought event (drought in the drought-to-flood event).

[0104] ④If the drought event in the soil drought-to-flood event occurs within the meteorological drought event, calculate the average SPEI value during the period from the start of the meteorological drought event to the start of the drought in the meteorological drought-to-flood event. If the average value is less than 0, the drought start time of the meteorological drought-to-flood event is extended to the start time of the corresponding meteorological drought event, and is considered as a successful pairing; if the condition is not met, the pairing relationship is cancelled.

[0105] ⑤Flood-drought event pairing method is opposite to the above process.

[0106] Specifically, based on the meteorological drought-to-flood events, soil drought-to-flood events, meteorological flood-to-drought events and soil flood-to-drought events of each spatial grid obtained by the preceding identification, targeted pairing operations are carried out: in drought-to-flood event pairing, firstly, the start and end time information of meteorological drought-to-flood events and soil drought-to-flood events on the same spatial grid are extracted and classified according to drought-to-flood types, and then the time difference between the soil flood start time and the meteorological flood start time in the grid is calculated If is less than or equal to the duration of the corresponding meteorological event plus 1 month, it is preliminarily determined that there is a corresponding relationship between the two, and then the difference between the soil drought start time and the meteorological drought start time in the paired event is further calculated If ≥ 0, it is directly determined that the pairing is successful, if < 0, it is necessary to determine whether the drought event in the soil drought-to-flood event occurs during a meteorological drought event of a non-drought-to-flood event, if such a situation exists, the average value of SPEI from the start of the meteorological drought event to the start of the drought in the meteorological drought-to-flood event is calculated, if the average value is less than 0, the drought start time of the meteorological drought-to-flood event is extended to the start time of the corresponding meteorological drought event and is considered as a successful pairing, if not satisfied, the pairing relationship is cancelled, and finally the drought-to-flood paired events of each spatial grid are obtained; in flood-to-drought event pairing, the process opposite to the above drought-to-flood event pairing is adopted, the start and end time information of meteorological flood-to-drought events and soil flood-to-drought events on the same spatial grid are extracted and classified according to flood-to-drought types, through calculation, determination of the corresponding time difference and necessary time adjustment and verification, the pairing of the two is completed, and the flood-to-drought paired events of each spatial grid are obtained. The pairing process realizes the accurate pairing of meteorological and soil level drought-flood events in the same spatial grid through multi-dimensional time difference determination, SPEI average value verification and targeted time adjustment rules, effectively avoids the mismatch or missing of different system events, ensures the strong correlation of meteorological and soil events in drought-to-flood and flood-to-drought paired events, and provides accurate event carriers for subsequent extraction of characteristic variable groups reflecting the coupling relationship between the two.

[0107] Step 105, extracting a plurality of meteorological and soil characteristic variable groups corresponding to the drought-to-flood paired events of each spatial grid and a plurality of meteorological and soil characteristic variable groups corresponding to the flood-to-drought paired events, and performing response analysis on the research area based on the corresponding plurality of meteorological and soil characteristic variable groups of each spatial grid, and outputting the target response analysis result corresponding to the research area.

[0108] The meteorological and soil characteristic variable group refers to a set of indexes with duration and severity of meteorological and soil events as cores, which are extracted from paired events of drought-to-flood or flood-to-drought of a spatial grid, and is a core data unit for response analysis.

[0109] The duration refers to a core characteristic index of a meteorological or soil event, which is obtained by counting cumulative months from a starting month to an ending month of the event, and is used to represent a time span of the event.

[0110] The severity refers to a core characteristic index of a meteorological or soil event, which is quantified by accumulating absolute values of a standardized index during the duration of the event, and is used to represent a strength of the event.

[0111] It should be noted that the multiple meteorological and soil characteristic variable groups corresponding to the paired events of drought-to-flood and the multiple meteorological and soil characteristic variable groups corresponding to the paired events of flood-to-drought of each spatial grid are extracted, wherein the meteorological and soil characteristic variable groups mainly include duration (obtained by counting cumulative months from a starting month to an ending month of an event) and severity (quantified by accumulating absolute values of a standardized precipitation evapotranspiration index and a standardized soil moisture index during the duration of an event), based on which response rules of soil to meteorological drought-flood sudden change events in a research region are analyzed, and target response analysis results corresponding to the research region are output.

[0112] Further, the step 105 can include the following sub-steps:

[0113] S51, extracting multiple meteorological and soil characteristic variable groups corresponding to paired events of drought-to-flood and multiple meteorological and soil characteristic variable groups corresponding to paired events of flood-to-drought of each spatial grid;

[0114] S52, calculating a correlation degree of each meteorological and soil characteristic variable group corresponding to each spatial grid;

[0115] S53, taking a meteorological and soil characteristic variable group corresponding to any correlation degree greater than a correlation degree threshold as a target meteorological and soil characteristic variable group;

[0116] S54, fitting marginal distributions of multiple target meteorological and soil characteristic variable groups corresponding to each spatial grid to obtain optimal marginal distributions corresponding to each target meteorological and soil characteristic variable group;

[0117] S55, constructing multiple joint distribution Copula models corresponding to each target meteorological and soil characteristic variable group based on the optimal marginal distributions corresponding to each target meteorological and soil characteristic variable group;

[0118] S56, screening out optimal Copula models corresponding to each target meteorological and soil characteristic variable group in the multiple joint distribution Copula models corresponding to each target meteorological and soil characteristic variable group, respectively.

[0119] S57, respectively, using each optimal Copula model according to the corresponding target meteorological soil characteristic variable group, the conditional probability and interval conditional probability corresponding to each target meteorological soil characteristic variable group are output;

[0120] S58, based on the conditional probability and interval conditional probability corresponding to each target meteorological soil characteristic variable group, the significance analysis and sensitivity analysis of the study area are carried out, and the corresponding significance analysis result and sensitivity analysis result of the study area are output.

[0121] The significance analysis result refers to the range, proportion and core association characteristics of different significant association levels in the study area obtained by sorting after dividing the significant levels based on the relative deviation of the conditional probability of each spatial grid and the random association level, which is used to clearly present the regional differences of the significant degree of the association between meteorological characteristic variables and soil characteristic variables.

[0122] The sensitivity analysis result refers to the regional distribution, coverage range and soil response characteristics of different sensitive meteorological intensity intervals in the study area obtained by sorting after determining the sensitive interval based on the interval conditional probability of each spatial grid in different meteorological intensity intervals, which is used to reveal the sensitive law and regional distribution characteristics of soil to different intensity meteorological events.

[0123] The conditional probability refers to the probability of the soil characteristic variable reaching the corresponding characteristic when the meteorological characteristic variable reaches a certain characteristic, which is calculated based on the optimal Copula model, and is used to quantify the association strength of the two.

[0124] The interval conditional probability refers to the probability of the soil characteristic variable reaching the corresponding characteristic when the meteorological characteristic variable is in a certain characteristic interval, which is calculated based on the optimal Copula model, and is the core index for sensitivity analysis.

[0125] It should be noted that for the correlation analysis of meteorological and soil drought and flood rapid turning event characteristic variables, the correlation between meteorological and soil event characteristic variables needs to be determined before constructing the Copula joint distribution, in order to determine the dependence relationship type of the two. The correlation between meteorological and soil characteristic variables is measured by using Spearman's Rank Correlation Coefficient. This method reflects the monotonicity through the rank of the variable, which can effectively describe the nonlinear dependence characteristics. The calculation formula of the correlation degree is as follows:

[0126] ;

[0127] wherein, The difference between the rank of the meteorological characteristic variable and the rank of the soil characteristic variable for the i-th sample (corresponding to the meteorological and soil characteristic variable sample of a certain spatial grid, such as the meteorological and soil characteristic variable data corresponding to a drought-to-flood or flood-to-drought pairing event of a certain grid) is the core intermediate parameter for calculating the Spearman rank correlation coefficient, and n is the number of samples. The result is used to evaluate the dependence strength and direction between the meteorological and soil drought-to-flood or flood-to-drought pairing event characteristic variables, and to provide a basis for subsequent Copula function type selection and joint distribution construction.

[0128] Based on the above, for the meteorological characteristic variable and the soil characteristic variable in each variable group, the Spearman rank correlation coefficient is used to calculate the correlation degree, which is calculated by the above formula to obtain the correlation degree of each meteorological and soil characteristic variable group corresponding to each spatial grid; then a correlation degree threshold is preset, and any meteorological and soil characteristic variable group with a correlation degree greater than the threshold is taken as the target meteorological and soil characteristic variable group. This process quantifies the correlation degree of meteorological and soil characteristic variables by the Spearman rank correlation coefficient, and then selects the strongly dependent target variable group by threshold, accurately identifying the strongly correlated characteristic dimension of the meteorological-soil drought-to-flood or flood-to-drought pairing event, and providing a reliable core object for subsequent construction of the joint distribution of the two.

[0129] Further, to depict the nonlinear dependence structure between the meteorological and soil events, the marginal distribution of each characteristic variable is first fitted. Seven commonly used probability distributions, including gamma distribution, exponential distribution, generalized Pareto distribution, generalized extreme value distribution, Weibull distribution, normal distribution and lognormal distribution, are selected as candidate models, the maximum likelihood estimation (MLE) is used to determine the distribution parameters, and the Kolmogorov-Smirnov (K-S) test and root mean square error (RMSE) are used to evaluate the goodness of fit, and the distribution with the smallest RMSE is selected as the optimal marginal distribution.

[0130] Subsequently, the Copula model of the joint distribution is constructed based on the optimal marginal distribution. The Copula function describes the dependence structure between multiple variables by connecting the marginal distribution, and its general form is:

[0131] ;

[0132] wherein, is the joint distribution function, indicating the joint distribution function of the meteorological characteristic variable and the soil characteristic variable corresponding to the drought-to-flood or flood-to-drought pairing event in the corresponding spatial grid, used to depict the joint probability distribution relationship between the two types of characteristic variables, and reflect the dependence structure of the meteorological-soil drought-to-flood or flood-to-drought pairing event characteristic variables; , The standardized marginal variable is one of the standardized marginal variables of the meteorological drought-flood abrupt change event characteristic variable (such as propagation time) in a spatial grid and the other is the standardized marginal variable of the soil response characteristic variable (such as the soil moisture variation coefficient) in the grid, which is a basic standardized unit for constructing the joint distribution of meteorological and soil characteristic variables; The generating function is a standardized marginal variable for connecting the meteorological characteristic variable and the soil characteristic variable, a function for constructing the dependence structure of the two, and a generating mapping function corresponding to the dependence relationship between the meteorological and soil drought-flood abrupt change event characteristic variables, which provides support for describing the association structure of the two.

[0133] Three Archimedean Copula functions (Frank, Clayton, and Gumbel) and two elliptical Copula functions (Gaussian and Student t) are selected as candidate models (as shown in Table 2), and the Squared Euclidean Distance (SED) is used to evaluate the goodness of fit. The Copula function with the smallest SED is regarded as the optimal model for describing the joint distribution relationship between the meteorological and soil event characteristic variables.

[0134] Table 2 Expression of three Archimedean Copula and two elliptical Copula

[0135]

[0136] wherein, The dependence parameter of the Copula function is used to describe the dependence strength and characteristics between the meteorological characteristic variable and the soil characteristic variable in the target spatial grid. Different Copulas correspond to different ranges, reflecting different preferences of the dependence relationship (such as upper / lower tail dependence, symmetric dependence, etc.); is the distribution function of the standard normal distribution; , is the quantile function for converting the standardized marginal variables u, v of the meteorological and soil characteristic variables into standard normal variables; , is the quantile function for converting the standardized marginal variables u, v of the meteorological and soil characteristic variables into t-distributed variables; k is the degree of freedom parameter of the t-distribution, which controls the tail thickness of the joint distribution of the meteorological and soil characteristic variables (the smaller k is, the thicker the tail is, and the higher the correlation strength of extreme values is); s, t are t-distributed variables converted from u, v, corresponding to the t-distributed values of the meteorological and soil characteristic variables, respectively.

[0137] Based on the above, according to the pre-screening of each spatial grid corresponding to a plurality of target meteorological soil characteristic variable groups, the marginal distribution fitting of the meteorological characteristic variables and soil characteristic variables in each target variable group is carried out respectively: selecting normal distribution, Gamma distribution, Weibull distribution and other common distribution types as candidate marginal distribution, using maximum likelihood estimation (MLE) method to estimate the parameters of each candidate distribution, and then quantifying the fitting effect by root mean square error (RMSE), AIC information criterion and other indicators, and selecting the distribution with the best fitting accuracy as the optimal marginal distribution of the corresponding variable in each target meteorological soil characteristic variable group; then based on the optimal marginal distribution corresponding to each target meteorological soil characteristic variable group, the meteorological and soil characteristic variables in the group are converted into standardized marginal variables (u, v) of uniform distribution, and then a plurality of joint distribution Copula models corresponding to each target variable group are constructed by selecting Clayton, Gumbel, Frank, Gaussian, tCopula and other types of Copula models, so as to realize the differentiated description of the dependence structure of meteorological-soil characteristic variables; finally, for a plurality of joint distribution Copula models corresponding to each target meteorological soil characteristic variable group, AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion) information criterion and Spearman rank correlation coefficient verification are used to comprehensively evaluate the fitting degree of each model to the dependence relationship between variables, and the model with the best evaluation index and the most accurate description of the dependence structure of the target variable group is selected as the optimal Copula model corresponding to each target meteorological soil characteristic variable group. Through the double optimization of multiple candidate distribution fitting and multiple model construction-screening, the accuracy of the distribution description of single meteorological and soil characteristic variables is ensured, and the optimal matching of the dependence structure between variables is realized, so as to accurately construct the joint distribution model of meteorological-soil characteristic variables, and provide a reliable model basis for subsequent meteorological-soil drought-flood sudden change risk assessment.

[0138] Further, Bayesian network is a structure combining probability theory and graph model, which can be used to describe the conditional dependence relationship between random variables. Taking meteorological event characteristic variables as conditional nodes (X) and soil event characteristic variables as target nodes (Y), based on the Copula joint distribution result, the conditional probability of meteorological event transmission to soil event is calculated:

[0139] ;

[0140] Wherein, X represents the meteorological characteristic variable (such as the intensity of the meteorological drought-flood event) in the paired event of drought-to-flood / flood-to-drought within the corresponding target spatial grid, which is the core parameter characterizing the intensity of the meteorological drought-flood abrupt change event; Y represents the soil characteristic variable (such as the level of the soil response event) in the paired event within the same spatial grid, which is the core parameter characterizing the degree of soil response to the meteorological drought-flood event; u represents the value of the meteorological characteristic variable X (i.e., a certain intensity value of the meteorological drought-flood event), used to define the intensity threshold of the meteorological event; v represents the value of the soil characteristic variable Y (i.e., a certain level value of the soil response event), used to define the level threshold of the soil response event. and Variables and The marginal cumulative distribution function, The joint cumulative probability of meteorological characteristic variable X and soil characteristic variable Y within the target spatial grid; Within the target spatial grid, "meteorological characteristic variables" Under the condition that "(the intensity of the meteorological event reaches u)," soil characteristic variables The conditional probability of "(soil response event reaches level v)" is used to measure the probability that a meteorological event of a certain intensity will trigger a corresponding soil event. When the conditional probability... When the value is ≥0.95, the corresponding meteorological event intensity u is considered as the propagation probability threshold for triggering a soil event of that level. This threshold reflects the significance level of the meteorological event's propagation to the soil system.

[0141] In addition, to analyze the sensitivity of soil events to meteorological events of different intensities, interval conditional probabilities were also calculated:

[0142] ;

[0143] in, , Given two values ​​for the meteorological characteristic variable X, define the interval of meteorological event intensity; For the meteorological characteristic variable X within the target spatial grid to be in the interval ≤X≤ The marginal probability, that is, the probability that the intensity of a meteorological event falls within this range; Within the target spatial grid, "meteorological characteristic variables are in the interval" Under the condition that "(the intensity of the meteorological event is within this range)", "soil characteristic variables" The interval conditional probability of "" is used to reveal the response patterns and sensitive intervals of soil events to meteorological events of different intensities; Within the target space grid, "X≤ The joint probability of "and Y≤v" minus "X≤ The joint probability of "and Y≤v" represents " ≤ X ≤ and Y ≤ v"; by comparing the conditional probabilities of different intervals, the response law and sensitive interval of soil events to the intensity change of meteorological events can be revealed.

[0144] Based on the above, according to the optimal Copula model corresponding to each spatial grid and the target meteorological and soil characteristic variable group obtained in the preamble, the marginal cumulative distribution function associated with each optimal Copula model (marginal cumulative distribution of meteorological characteristic variable X) and the joint cumulative probability (joint cumulative probability of X and Y) are called respectively to calculate the conditional probability of "soil characteristic variable Y reaching level v when meteorological characteristic variable X reaches intensity u" At the same time, the meteorological characteristic variable is divided into low, medium and high intensity intervals according to the quantile, and the interval conditional probability corresponding to each interval is calculated by using the marginal cumulative distribution and joint cumulative probability of the interval endpoints , and the conditional probability and interval conditional probability corresponding to each target meteorological and soil characteristic variable group are output.

[0145] Subsequently, the significance analysis of the study area is carried out: taking the conditional probability of each spatial grid as the core basis, first set the probability judgment standard of significant correlation (i.e. the threshold of the deviation of conditional probability from the random correlation level, and the random correlation level is the marginal probability of the soil characteristic variable itself reaching the corresponding threshold), by calculating the relative deviation of the conditional probability of each spatial grid from the random correlation level, the high, medium and low three significant correlation levels are divided (the larger the relative deviation, the more significant the correlation), and then the range of the region corresponding to different significant levels, the proportion of the region, and the core correlation characteristics of each level region are sorted out, forming the significance analysis result; at the same time, the sensitivity analysis is carried out: based on the interval conditional probability corresponding to each spatial grid in different meteorological intensity intervals (low, medium and high), the probability value of different intervals in the same grid is compared, and the interval with the highest probability value is determined as the sensitive meteorological intensity interval of the grid, then the sensitive interval information of all spatial grids is integrated, the regional distribution and coverage range corresponding to different sensitive intervals in the study area, and the soil response characteristics of each sensitive interval region are clarified, forming the sensitivity analysis result, and finally the significance analysis result and the sensitivity analysis result corresponding to the study area are output. Through the accurate quantification of conditional probability and interval conditional probability, this analysis process clearly defines the significant differences of meteorological-soil characteristic variable correlation in the study area and the sensitive interval distribution of soil to meteorological events, making the regional heterogeneity of meteorological-soil coupling law more clear and specific, providing precise characteristic basis for formulating targeted disaster prevention and mitigation strategies in different regions and different meteorological intensity levels in the study area, and effectively improving the support accuracy of the analysis result for disaster prevention and mitigation work.

[0146] It is worth mentioning that, for the significant analysis result, the high significant correlation region is listed as the key prevention and control region, the soil state dynamic monitoring equipment is preferentially configured, the monitoring frequency is encrypted, the secondary disaster protection engineering such as landslide and waterlogging is deployed in advance, and the resource allocation of the medium and low significant correlation region is optimized as needed to avoid excessive prevention and control; combined with the sensitivity analysis result, differentiating early warning and response schemes are formulated for regions in different sensitive intensity intervals: for the region sensitive to low-intensity meteorological events, daily meteorological monitoring and early warning need to be strengthened, and mild disaster emergency preparation is done in advance; for the region sensitive to high-intensity meteorological events, the focus is on improving the emergency plan for extreme weather, and sufficient emergency supplies are reserved, and when the meteorological event reaches the threshold of the sensitive interval, personnel transfer and rescue work are quickly started, so that the disaster prevention and reduction measures are accurately matched with the meteorological-soil correlation characteristics and sensitive response law of different regions, the prevention and control efficiency and pertinence are improved, and high-quality scientific support is provided.

[0147] Optionally, it further comprises:

[0148] The soil moisture variation coefficient corresponding to the meteorological drought-flood event in the drought-flood pairing event and the soil moisture variation coefficient corresponding to the meteorological flood-drought event in the flood-drought pairing event of each spatial grid are calculated.

[0149] Based on the corresponding soil moisture variation coefficient of each spatial grid, the soil moisture response intensity difference analysis of the research region is carried out, and the corresponding soil moisture response intensity difference analysis result of the research region is obtained.

[0150] The composite time sequence curve corresponding to the meteorological drought-flood event in the drought-flood pairing event and the composite time sequence curve corresponding to the meteorological flood-drought event in the flood-drought pairing event of each spatial grid are calculated.

[0151] Based on the corresponding composite time sequence curve of each spatial grid, the soil moisture response difference analysis of the research region is carried out, and the corresponding response difference analysis result of the research region is output.

[0152] The propagation time corresponding to the meteorological drought-flood event in the drought-flood pairing event and the propagation time corresponding to the meteorological flood-drought event in the flood-drought pairing event of each spatial grid are calculated.

[0153] Based on the corresponding propagation time of each spatial grid, the response time lag analysis of the research region is carried out, and the corresponding response time lag analysis result of the research region is output.

[0154] It should be noted that, for the calculation of the soil moisture variation coefficient, the coefficient of variation (CV) of the soil moisture during the meteorological drought-flood event is calculated, which is used to quantify the fluctuation degree of the soil moisture change during the meteorological event. The present application calculates different types of events respectively:

[0155] ① For the event of drought to flood, take the soil moisture sequence during the period from the beginning of meteorological drought to the end of meteorological flood to calculate CV;

[0156] ② For the event of flood to drought, take the soil moisture sequence during the period from the beginning of meteorological flood to the end of meteorological drought to calculate CV.

[0157] The calculation formula of the coefficient of variation is as follows:

[0158] ;

[0159] Among them, is the average soil moisture value of the period, is the standard deviation of the period. The specific expression is:

[0160] ;

[0161] Among them, is the i-th soil moisture data (such as the monthly soil moisture value of each month in the period) in the corresponding spatial grid during the meteorological drought-flood rapid transition related period; is the sample number of soil moisture data in the corresponding spatial grid during the meteorological drought-flood rapid transition related period; the larger the CV value, the stronger the soil moisture fluctuation during the period, and the more unstable the change. This index reflects the intensity of the response of soil moisture to the meteorological drought-flood rapid transition process, and is an important parameter for characterizing the influence intensity of meteorological events on soil system.

[0162] Based on the above basis, for the drought-to-flood pairing event, the soil moisture sequence during the meteorological drought to the end of the meteorological flood is taken, for the flood-to-drought pairing event, the soil moisture sequence during the meteorological flood to the end of the meteorological drought is taken, and then combined with the soil moisture data and the number of samples in the corresponding spatial grid during the period, the soil moisture variation coefficient corresponding to the drought-to-flood pairing event of each spatial grid and the soil moisture variation coefficient corresponding to the flood-to-drought pairing event are obtained; then based on the two types of soil moisture variation coefficients of each spatial grid, the calculation results of all spatial grids are integrated, the distribution characteristics of the soil moisture variation coefficients in different regions of the research area are analyzed, and it is clear which regions have stronger soil moisture fluctuation and which regions are more stable, and the difference analysis result of the soil moisture response intensity corresponding to the research area is obtained. The result points to the soil moisture variation coefficient of all spatial grids in the integrated research area, and the spatial distribution characteristics of the soil moisture fluctuation degree in the region obtained by analysis can clearly show the intensity difference of the influence of meteorological drought and flood sudden change in different regions of the research area. The present application calculates the soil moisture variation coefficient by selecting the accurate period sequence for different types of pairing events, realizes the quantitative characterization of the influence intensity of meteorological drought and flood sudden change on the soil system, and then analyzes the response intensity difference at the regional level by integrating the spatial grid data, accurately describes the spatial heterogeneity of the meteorological-soil system coupling relationship in the research area, and provides more targeted scientific basis for the disaster prevention and mitigation work in different regions of the research area.

[0163] Further, for soil moisture change trend calculation, in order to analyze the dynamic response law of soil moisture to meteorological process during meteorological drought and flood sudden change event, a composite time sequence of standardized soil moisture index (SSMI) is constructed, and a change trend index is calculated, the specific steps are as follows:

[0164] ① Data extraction and time window setting: taking meteorological drought and flood sudden change event as the analysis unit, extracting SSMI data of each event corresponding to the grid in a certain time range (monthly scale) before and after the event. The drought-to-flood event takes the flood starting time as the base month (marked as 0 month), and the flood-to-drought event takes the drought starting time as the base month (marked as 0 month), and sets a time window of 6 months before and after (i.e. M-6 to M+6), which is used to capture the continuous change process before and after the event.

[0165] ② Event alignment and composite sequence construction: all events of the same type (drought-to-flood or flood-to-drought) are time-aligned according to the base month, the sample average of SSMI value at each relative time (month offset) is calculated, and the composite time sequence curve is obtained:

[0166] ;

[0167] Among them, is the composite average value of the relative base month t, is the SSMI value of the i-th meteorological drought-flood abrupt event at the relative time t, is the number of event samples at this time. The composite sequence reflects the overall response characteristics of soil moisture to meteorological processes under the same type of event.

[0168] Based on the above, for the meteorological drought-flood abrupt event corresponding to the drought-flood pair event, set the flood start time of the event as the base month (marked as 0 month), and set a time window of 6 months before and after (i.e. M-6 to M+6), extract the monthly scale standardized soil moisture index (SSMI) data of the corresponding spatial grid in the time range; for the meteorological flood-drought pair event corresponding to the flood-drought pair event, set the drought start time of the event as the base month (marked as 0 month), and also set a time window of 6 months before and after, extract the monthly scale standardized soil moisture index (SSMI) data of the corresponding spatial grid; then perform event alignment and composite sequence construction, align all events of the same type (drought-flood or flood-drought) according to the base month, calculate the sample average for the SSMI value at each relative time t, and obtain the composite time sequence curve of the meteorological drought-flood abrupt event corresponding to the drought-flood pair event, and the composite time sequence curve of the meteorological flood-drought event corresponding to the flood-drought pair event; then based on the composite time sequence curve corresponding to each spatial grid, integrate the curve characteristics of all spatial grids in the study area, analyze the differences in the change trend and fluctuation rhythm of SSMI before and after the meteorological drought-flood abrupt event, and output the response difference analysis result corresponding to the study area. The result points to the integration of the composite time sequence curve characteristics of all spatial grids in the study area, and the analysis of the dynamic response difference of soil moisture to meteorological drought-flood abrupt events in the region. The invention can clearly distinguish the difference in soil moisture change trend and fluctuation rhythm before and after the event in different regions. By setting a precise time window before and after the event to capture the continuous change of soil moisture, combining event alignment and sample average to construct a composite time sequence curve, the invention realizes the quantitative characterization of the dynamic response rule of soil moisture to meteorological drought-flood abrupt events, and through the difference analysis of the curve characteristics at the regional level, the response heterogeneity of soil moisture to meteorological drought-flood abrupt events in the study area is accurately described, which provides more detailed dynamic response basis for the development of targeted disaster prevention and mitigation measures in different regions in the study area.

[0169] Further, for the propagation time calculation of meteorological events to soil events, in order to quantitatively characterize the propagation characteristics of meteorological drought-flood abrupt events to soil systems, based on the one-to-one matching samples of meteorological events and soil events, the propagation time of meteorological events to soil events is calculated, so as to quantify the response time lag of soil to meteorological events. The specific method is as follows:

[0170] For the drought-to-flood event, the propagation time is defined as the starting time of the soil flood event minus the starting time of the meteorological flood event, that is, the time interval experienced from the start of meteorological flood to the soil flood response.

[0171]

[0172] Based on the above, for the meteorological drought-to-flood event corresponding to the drought-to-flood paired event, the starting time of the soil drought event in the paired event is subtracted from the starting time of the meteorological drought event to obtain the propagation time corresponding to the event, which represents the time interval experienced from the start of meteorological drought to the soil drought response; for the meteorological flood-to-drought event corresponding to the flood-to-drought paired event, the starting time of the soil flood event in the paired event is subtracted from the starting time of the meteorological flood event to obtain the propagation time corresponding to the event, which represents the time interval experienced from the start of meteorological flood to the soil flood response, and then the propagation time corresponding to the drought-to-flood paired event and the propagation time corresponding to the flood-to-drought paired event for each spatial grid are obtained; then, based on the propagation times corresponding to each spatial grid, the propagation time data of all spatial grids in the study area are integrated, the length distribution and spatial aggregation characteristics of the propagation time in different regions are analyzed, the fast and slow differences of the soil in the study area in response to meteorological drought and flood are clarified, and the response time lag analysis result corresponding to the study area is output. The result refers to the analysis result obtained by integrating the propagation time data of all spatial grids in the study area, which can present the response time lag distribution characteristics of the soil in different regions in the study area in response to meteorological drought and flood, and clarify the fast and slow differences of the soil in different regions in response to meteorological events; the present application quantitatively describes the propagation time of meteorological drought and flood events to the soil system through the calculation method of the starting time difference, accurately quantifies the response time lag of the soil to meteorological events, and clearly presents the spatial heterogeneity of the response time sequence relationship of the meteorological-soil system in the study area through the time lag distribution analysis at the regional level, which provides accurate time lag basis for formulating differentiated disaster prevention and mitigation warning schemes for different regions in the study area, and effectively improves the support quality of the analysis result for disaster prevention and mitigation work.

[0173] ​​It is worth mentioning that, based on the analysis results of the difference in soil moisture response intensity, the areas with severe soil moisture fluctuations (high response intensity) are identified, which are prone to secondary disasters such as landslides caused by sudden changes in soil state. Therefore, the soil state dynamic monitoring in these areas should be strengthened, and protective projects should be deployed in advance. Based on the analysis results of the response difference, the change rhythm of soil moisture in different regions before and after the rapid transition of drought and flood is determined (e.g., some regions respond quickly, and some regions change smoothly). The time node and monitoring frequency of the early warning should be adjusted accordingly, such as shortening the early warning interval for regions with rapid response rhythm. Based on the response time lag analysis results, the response speed of soil to meteorological events is distinguished: for regions with short time lag, emergency resources should be quickly allocated after the meteorological drought and flood signal appears; for regions with long time lag, materials and personnel can be prepared in advance during the time lag window. Through such precise measures in different regions and time sequences, disaster prevention and mitigation measures are matched with the soil response characteristics of different regions, providing more practical and high-quality scientific support.

[0174] For example, meteorological and soil data from 2001 to 2023 worldwide are collected. The meteorological dry and wet index used in this example is the standardized precipitation evapotranspiration index (SPEI), and the data is from the global SPEI database. The soil moisture data is selected from the fifth-generation reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts, including soil moisture data at depths of 0-7 cm, 7-28 cm, and 28-100 cm. The soil moisture data at multiple depths is integrated to obtain comprehensive soil moisture data at 0-100 cm, which is used for subsequent analysis.

[0175] Then, the standardized soil moisture index (SSMI) is calculated. Based on the obtained 0-100 cm soil moisture data, the standardized soil moisture index (SSMI) of each time series and spatial grid is calculated using the standardization method.

[0176] Based on the SPEI and SSMI data, the meteorological drought and flood rapid transition events and soil drought and flood rapid transition events in the study area are identified and analyzed in pairs using the run theory. Specifically, the threshold values of SPEI or SSMI are set to ±0.5, and when the index exceeds this threshold for two consecutive months or more, it is defined as a flood or drought event. The duration of the rapid transition is set to one month.

[0177] For the identified meteorological drought and flood rapid transition events, the change rate, change trend, and propagation time of meteorological events to soil events during the corresponding period are calculated. Specifically, by analyzing the dynamic changes of soil moisture during the occurrence of meteorological events, the response characteristics of soil moisture to meteorological drought and flood rapid transition events are quantified. Based on this, the results in different climate zones are compared and analyzed in combination with the Koppen-Geiger climate zone data, to reveal the influence of climate types on the propagation law of drought and flood. Figure 2The boxplot of the variability of soil moisture during the meteorological drought-flood events in different climate zones is shown. Figure 3 The average time series evolution of soil moisture during the meteorological drought-flood events is shown. Figure 4 The proportion distribution of the propagation time of meteorological drought-flood events to soil drought-flood events in different time periods in different climate zones is shown.

[0178] The joint distribution relationship between the duration of meteorological and soil drought-flood events is established based on the Copula function model. First, the marginal distribution of the duration of meteorological and soil drought-flood events is fitted, and the optimal marginal distribution function is selected; then, the Copula function is used to establish the joint distribution model of the two, to describe the dependence structure of meteorological events and soil events in the duration. On this basis, the conditional probability of the propagation of meteorological events to soil events under different intensity levels is calculated combined with the Bayesian network model, so as to determine the propagation probability threshold. Table 3 shows the optimal marginal fitting distribution of the duration of meteorological and soil drought-flood events and the corresponding Copula function type in 7 climate zones; Table 4 lists the duration threshold of the propagation of meteorological drought-flood events to soil drought-flood events.

[0179] Table 3 Optimal marginal fitting distribution of the duration of meteorological and soil drought-flood events and the corresponding Copula function type

[0180]

[0181] Table 4 Duration threshold of the propagation of meteorological drought-flood events to soil drought-flood events

[0182]

[0183] As a comparison of technical effects, it can be referred to the existing technology, and the sudden change of drought and flood refers to a kind of extreme compound disaster event that a basin changes from drought to flood or from flood to drought in a short time. In recent years, affected by global climate change, the frequency and intensity of the sudden change of drought and flood continue to rise, which has a significant impact on agricultural production, water resources management and ecological environment safety. The identification and analysis of the sudden change of drought and flood are of great significance to improve the water resources regulation capacity and the disaster prevention and reduction level of the basin. The existing researches mainly identify the sudden change of drought and flood based on different types of drought indexes such as meteorology, hydrology or soil. Similar to the mutual transmission of drought events in different systems, the transmission relationship may also exist between different types of sudden change of drought and flood. For example, the meteorological sudden change of drought and flood changes the evaporation of the earth's surface and the soil moisture, and then affects the water redistribution process of the earth's surface, thereby causing or adjusting the change of soil drought and flood. However, the current researches mainly focus on the transmission mechanism of different types of drought events, and the quantitative research on the transmission relationship between meteorological and soil sudden change of drought and flood is still relatively lacking. In addition, there is a significant nonlinear dependence relationship and time lag effect between meteorological drought and flood and soil drought and flood, and the traditional linear correlation or empirical statistical method is difficult to accurately describe the complex response mechanism between them.

[0184] In view of the above problems, the present application provides a method for analyzing the response of soil sudden change of drought and flood to meteorological sudden change of drought and flood. The method builds a joint distribution function of the characteristics of meteorological and soil sudden change of drought and flood, calculates the conditional probability under different intensity levels by combining the Bayesian network model, determines the probability threshold of the transmission of meteorological events to soil events, realizes the quantitative description of the mutual response characteristics of meteorological drought and flood and soil drought and flood and the identification of the transmission path, and provides a scientific basis for the monitoring, prediction and risk prevention and control of the sudden change of drought and flood disaster in the basin.

[0185] Specifically, the standardized precipitation evapotranspiration index (SPEI) data and the soil moisture data of multiple depths of a target region are obtained, and the standardized soil moisture index (SSMI) is constructed based on the soil moisture data; the meteorological sudden change of drought and flood and the soil sudden change of drought and flood in the region are identified by using the SPEI and the SSMI respectively; the identified meteorological events and soil events are time-matched, and the change rate, change trend and transmission time of the meteorological sudden change of drought and flood to the soil sudden change of drought and flood are calculated. Then, the probability threshold of the transmission of meteorological events to soil events is calculated based on the Copula function and the Bayesian network model. The present application provides a systematic analysis framework for the transmission of the sudden change of drought and flood, which can comprehensively describe the spatio-temporal response characteristics and transmission law between the meteorological and soil systems, and provide a scientific basis for evaluating the interaction mechanism of extreme drought and flood events and the regional comprehensive disaster prevention and reduction under the changing environment.

[0186] In summary, the application can comprehensively depict the nonlinear dependence relationship and propagation mechanism between the meteorological and soil systems, thereby achieving more comprehensive and accurate monitoring and prediction of regional dry and wet state changes. By quantitatively identifying the propagation characteristics and thresholds of meteorological drought and flood sudden changes to soil drought and flood sudden changes, the identification accuracy and early warning capability of drought and flood sudden change events can be effectively improved, providing scientific basis for mechanism analysis, disaster prevention and mitigation, and water resource regulation of basin drought and flood disasters, and having important application value and theoretical significance.

[0187] In the embodiment of the application, the above technical scheme of the application provides a soil drought and flood sudden change response analysis method for meteorological drought and flood sudden change, obtains original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of a plurality of spatial grids in a research area in each month; pre-processes the original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of each spatial grid in each month, and outputs monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month; identifies drought and flood sudden change events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month by using event identification and merging rules, and outputs a plurality of target drought and flood sudden change events corresponding to each spatial grid; pairs the plurality of target drought and flood sudden change events corresponding to each spatial grid, and outputs drought-to-flood pairing events and flood-to-drought pairing events of each spatial grid; extracts a plurality of meteorological and soil characteristic variable groups corresponding to the drought-to-flood pairing events of each spatial grid and a plurality of meteorological and soil characteristic variable groups corresponding to the flood-to-drought pairing events, and performs response analysis on the research area based on the corresponding plurality of meteorological and soil characteristic variable groups of each spatial grid, and outputs a target response analysis result corresponding to the research area; based on the above scheme, the comparability and reliability of the precipitation evapotranspiration and soil moisture data are ensured by standardization preprocessing, and the drought and flood sudden change events are identified and paired based on the standardized index and the event identification and merging rules, the accurate matching of the meteorological and soil level drought and flood sudden change events is realized, the multi-dimensional coupling relationship between the meteorological drought and flood sudden change and the soil drought and flood sudden change under different spatial grids is comprehensively captured by extracting a plurality of meteorological and soil characteristic variable groups and focusing on the response analysis of the research area, the limitations of the traditional method that only focuses on a single drought event propagation and is difficult to depict complex coupling rules are broken; and finally, the target response analysis result of the research area is output, the local laws of each spatial grid are integrated to form a macroscopic understanding, and the coupling laws of the meteorological and soil systems are accurately reflected, thereby providing strong pertinence and high reliability high-quality scientific basis for disaster prevention and mitigation decision-making of the research area.

[0188] Please refer to Figure 5 , Figure 5 The structural block diagram of a soil drought and flood sudden change response analysis system for the second embodiment of the application is provided.

[0189] The application provides a soil drought-flood abrupt change response analysis system for meteorological drought-flood abrupt change, which comprises:

[0190] The acquisition module 501 is configured to acquire original precipitation evapotranspiration index data and original multi-layer deep soil humidity data of a plurality of spatial grids in a research area in each month;

[0191] The preprocessing module 502 is configured to preprocess the original precipitation evapotranspiration index data and the original multi-layer deep soil humidity data of each spatial grid in each month, and output monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil humidity index of each spatial grid in each month;

[0192] The identification module 503 is configured to identify drought-flood abrupt change events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil humidity index of each spatial grid in each month by using event identification and merging rules, and output a plurality of target drought-flood abrupt change events corresponding to each spatial grid;

[0193] The pairing module 504 is configured to pair the plurality of target drought-flood abrupt change events corresponding to each spatial grid, and output drought-flood abrupt change pairing events and flood-drought abrupt change pairing events of each spatial grid;

[0194] The analysis module 505 is configured to extract a plurality of meteorological soil characteristic variable groups corresponding to the drought-flood abrupt change pairing events of each spatial grid and a plurality of meteorological soil characteristic variable groups corresponding to the flood-drought abrupt change pairing events, and perform response analysis on the research area based on the corresponding plurality of meteorological soil characteristic variable groups of each spatial grid, and output a target response analysis result corresponding to the research area.

[0195] In an optional system embodiment, the system further comprises:

[0196] The first module is configured to calculate a soil humidity variation coefficient corresponding to a meteorological drought-flood abrupt change event in the drought-flood abrupt change pairing event of each spatial grid, and a soil humidity variation coefficient corresponding to a meteorological flood-drought abrupt change event in the flood-drought abrupt change pairing event;

[0197] The second module is configured to perform soil humidity response intensity difference analysis on the research area based on the corresponding soil humidity variation coefficients of each spatial grid, and obtain a soil humidity response intensity difference analysis result corresponding to the research area;

[0198] The third module is configured to calculate a composite time sequence curve corresponding to the meteorological drought-flood abrupt change event in the drought-flood abrupt change pairing event of each spatial grid, and a composite time sequence curve corresponding to the meteorological flood-drought abrupt change event in the flood-drought abrupt change pairing event;

[0199] The fourth module is configured to perform soil humidity response difference analysis on the research area based on the corresponding composite time sequence curves of each spatial grid, and output a response difference analysis result corresponding to the research area.

[0200] a fifth module configured to calculate a propagation time corresponding to a meteorological drought-flood event in a drought-flood pair event of each spatial grid, and a propagation time corresponding to a meteorological flood-drought event in a flood-drought pair event;

[0201] a sixth module configured to perform a response time lag analysis on the study area based on the corresponding propagation time of each spatial grid, and output a corresponding response time lag analysis result of the study area.

[0202] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system and modules can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0203] The embodiment of the present application also provides a computer device, comprising a memory and a processor, and the memory stores a computer program; when the computer program is executed by the processor, the processor executes the steps of the soil drought-flood rapid change response analysis method for meteorological drought-flood rapid change of the above-described embodiment.

[0204] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program / instruction, and the computer program / instruction is executed by a processor to implement the steps of the soil drought-flood rapid change response analysis method for meteorological drought-flood rapid change of the above-described embodiment.

[0205] The embodiment of the present application also provides a computer program product, comprising a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the soil drought-flood rapid change response analysis method for meteorological drought-flood rapid change of the above-described embodiment.

[0206] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing the response of soil drought-flood abrupt change to meteorological drought-flood abrupt change, characterized in that, The method comprises the following steps: obtaining original precipitation evapotranspiration index data and original multi-layer soil moisture data of a plurality of spatial grids in a study area in each month; preprocessing the original precipitation evapotranspiration index data and the original multi-layer soil moisture data of each spatial grid in each month, and outputting monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month; identifying drought-flood rapid change events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month by using event identification and merging rules, and outputting a plurality of target drought-flood rapid change events corresponding to each spatial grid; pairing the plurality of target drought-flood rapid change events corresponding to each spatial grid, and outputting drought-flood change pairing events and flood-drought change pairing events of each spatial grid; extracting a plurality of meteorological soil characteristic variable groups corresponding to the drought-flood change pairing events and a plurality of meteorological soil characteristic variable groups corresponding to the flood-drought change pairing events of each spatial grid, and performing response analysis on the study area based on the corresponding plurality of meteorological soil characteristic variable groups of each spatial grid, and outputting a target response analysis result corresponding to the study area.

2. The method according to claim 1, wherein the soil drought-flood abrupt change response analysis method is characterized by, The preprocessing of the original precipitation evapotranspiration index data and the original multi-layer soil moisture data of each spatial grid in each month, and the output of the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month comprises the following steps: standardizing and unifying the time scale of the original precipitation evapotranspiration index data and the original multi-layer soil moisture data of each spatial grid in each month in sequence, and outputting the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized multi-layer soil moisture data of each spatial grid in each month; integrating the monthly scale standardized multi-layer soil moisture data of each spatial grid in each month to obtain a monthly scale standardized comprehensive soil moisture value of each spatial grid in each month; based on the monthly scale standardized comprehensive soil moisture value of each spatial grid in each month, calculating the monthly scale standardized soil moisture index of each spatial grid in each month.

3. The method according to claim 1, wherein the soil drought-flood abrupt change response analysis method is characterized by, The event identification and merging rules include drought-flood event identification rules, drought-flood event merging rules, and drought-flood rapid change event identification rules; the identification of drought-flood rapid change events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and the monthly scale standardized soil moisture index of each spatial grid in each month by using the event identification and merging rules, and the output of a plurality of target drought-flood rapid change events corresponding to each spatial grid comprises the following steps: identifying drought-flood events of each spatial grid in each month according to the monthly scale standardized soil moisture index and the monthly scale standardized precipitation evapotranspiration index data by using the drought-flood event identification rules, and outputting initial drought-flood events of each spatial grid in a plurality of time periods; merging the initial drought-flood events of each spatial grid in a plurality of time periods by using the drought-flood event merging rules, and outputting a plurality of merged drought-flood events corresponding to each spatial grid; The dry-wet abrupt change event identification rule is used to identify dry-wet abrupt change events corresponding to the plurality of combined dry-wet events of each spatial grid, and a plurality of target dry-wet abrupt change events corresponding to each spatial grid are output.

4. The method according to claim 1, wherein the soil drought-flood abrupt change response analysis method is characterized by, The target dry-wet abrupt change events include meteorological dry-to-wet events, soil dry-to-wet events, meteorological wet-to-dry events, and soil wet-to-dry events; the pairing of the plurality of target dry-wet abrupt change events corresponding to each spatial grid is performed to output dry-to-wet paired events and wet-to-dry paired events of each spatial grid, which include: Dry-to-wet event pairing of each meteorological dry-to-wet event and each soil dry-to-wet event of each spatial grid is performed to obtain dry-to-wet paired events of each spatial grid; Wet-to-dry event pairing of each meteorological wet-to-dry event and each soil wet-to-dry event of each spatial grid is performed to obtain wet-to-dry paired events of each spatial grid.

5. The method according to claim 1, wherein the soil drought-flood abrupt change response analysis method is characterized by, The plurality of meteorological and soil characteristic variable groups corresponding to the dry-to-wet paired events and the plurality of meteorological and soil characteristic variable groups corresponding to the wet-to-dry paired events of each spatial grid are extracted, and response analysis of the study area is performed based on the corresponding plurality of meteorological and soil characteristic variable groups of each spatial grid to output a target response analysis result corresponding to the study area, which includes: The plurality of meteorological and soil characteristic variable groups corresponding to the dry-to-wet paired events and the plurality of meteorological and soil characteristic variable groups corresponding to the wet-to-dry paired events of each spatial grid are extracted; The correlation degree of each meteorological and soil characteristic variable group corresponding to each spatial grid is calculated; Any meteorological and soil characteristic variable group corresponding to a correlation degree greater than a correlation degree threshold value is taken as a target meteorological and soil characteristic variable group; The marginal distribution of each target meteorological and soil characteristic variable group corresponding to each spatial grid is fitted to obtain an optimal marginal distribution corresponding to each target meteorological and soil characteristic variable group; A plurality of joint distribution Copula models corresponding to each target meteorological and soil characteristic variable group are constructed based on the optimal marginal distribution corresponding to each target meteorological and soil characteristic variable group; In each joint distribution Copula model corresponding to each target meteorological and soil characteristic variable group, an optimal Copula model corresponding to each target meteorological and soil characteristic variable group is screened out; The conditional probability and interval conditional probability corresponding to each target meteorological and soil characteristic variable group are output by performing probability calculation on the corresponding target meteorological and soil characteristic variable group using each optimal Copula model; Significance analysis and sensitivity analysis of the study area are performed based on the conditional probability and interval conditional probability corresponding to each target meteorological and soil characteristic variable group to output significance analysis results and sensitivity analysis results corresponding to the study area.

6. The method according to claim 1, wherein the soil drought-flood abrupt change response analysis method is characterized by, Further including: The soil moisture variation coefficient corresponding to the meteorological dry-to-wet event in the dry-to-wet paired event of each spatial grid and the soil moisture variation coefficient corresponding to the meteorological wet-to-dry event in the wet-to-dry paired event of each spatial grid are calculated; performing soil moisture response intensity difference analysis on the study area based on the corresponding soil moisture variation coefficients of the spatial grids, to obtain the corresponding soil moisture response intensity difference analysis result of the study area; calculating a composite time series curve corresponding to a meteorological drought-flood event in the drought-to-flood pairing event and a composite time series curve corresponding to a meteorological flood-to-drought event in the flood-to-drought pairing event of each spatial grid; performing soil moisture response difference analysis on the study area based on the corresponding composite time series curves of each spatial grid, and outputting the corresponding response difference analysis result of the study area; calculating a propagation time corresponding to a meteorological drought-flood event in the drought-to-flood pairing event and a propagation time corresponding to a meteorological flood-to-drought event in the flood-to-drought pairing event of each spatial grid; performing response time lag analysis on the study area based on the corresponding propagation times of each spatial grid, and outputting the corresponding response time lag analysis result of the study area.

7. A system for analyzing the response of soil drought-flood abrupt change to meteorological drought-flood abrupt change, characterized in that, It comprises: an acquisition module for acquiring original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of a plurality of spatial grids in a study area in each month; a preprocessing module for preprocessing the original precipitation evapotranspiration index data and multi-layer depth soil moisture original data of each spatial grid in each month, and outputting monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month; an identification module for identifying drought-flood rapid transition events of each spatial grid according to the monthly scale standardized precipitation evapotranspiration index data and monthly scale standardized soil moisture index of each spatial grid in each month by using event identification and merging rules, and outputting a plurality of target drought-flood rapid transition events corresponding to each spatial grid; a pairing module for pairing a plurality of target drought-flood rapid transition events corresponding to each spatial grid, and outputting drought-to-flood pairing events and flood-to-drought pairing events of each spatial grid; an analysis module for extracting a plurality of meteorological soil characteristic variable groups corresponding to the drought-to-flood pairing events and a plurality of meteorological soil characteristic variable groups corresponding to the flood-to-drought pairing events of each spatial grid, and performing response analysis on the study area based on the corresponding plurality of meteorological soil characteristic variable groups of each spatial grid, and outputting the corresponding target response analysis result of the study area.

8. An electronic device, comprising: It comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the soil drought-flood rapid transition response analysis method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to realize the soil drought-flood rapid transition response analysis method according to any one of claims 1-6.

10. A computer program product, characterised in that, The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the soil drought-flood rapid transition response analysis method according to any one of claims 1-6.

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