A method and device for extracting the spatiotemporal evolution process of drought-flood rapid transition events

Abstract

The invention discloses a method and device for extracting the spatiotemporal evolution process of drought-flood sudden transition events, comprising rasterizing a research basin, determining the rasterized basin, and obtaining precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month; then, according to the precipitation evapotranspiration data of each grid point in each month, determining the standardized precipitation evapotranspiration index of each grid point in each month; using a three-dimensional connected domain analysis method to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, and generating multiple aggregated drought-flood events; based on preset disaster conditions, extracting the spatiotemporal evolution process of each aggregated drought-flood event, and generating an extraction result of the evolution process of the drought-flood sudden transition event; solving the technical problem that the spatiotemporal evolution trend of the drought-flood sudden transition event cannot be accurately identified due to the existing research on the spatiotemporal evolution of the drought-flood sudden transition event.

Description

Technical Field

[0001] The present invention relates to the field of hydrology and water resources application technology, and in particular to a method and device for extracting the spatiotemporal evolution process of drought-flood rapid transition events. Background Art

[0002] Dry-Wet Abrupt Alternation (DWAA) is a compound extreme event, including drought-waterlogging (DW) and waterlogging-drought (WD) events. Climate change and human activities are important components of global change, and their impact on DWAA events is a hot topic in global water science research.

[0003] Climate change (including natural climate variation and anthropogenic climate change) will affect the runoff process of the basin, thus affecting the occurrence and evolution of drought-flood transition events. Human activities (artificial water extraction, reservoir construction, etc.) will affect the natural water cycle process, change the original rainfall-runoff relationship, and also have different degrees of impact on drought-flood transition; as a hot spot in the current research frontier of compound disasters, the accurate identification and extraction of drought-flood transition events is also a difficult problem in the study of drought-flood transition. Drought-flood transition events often evolve together in space and time, showing three-dimensional spatiotemporal (latitude × longitude × time) proximity characteristics.

[0004] Most of the existing studies on the spatiotemporal evolution of drought-flood transition events focus on the evolution characteristics of drought-flood states in time series, and only focus on the independent evolution of drought and flood in spatial or temporal dimensions, which makes it impossible to accurately identify the spatiotemporal evolution trends of drought-flood transition events. Summary of the invention

[0005] The present invention provides a method and device for extracting the spatiotemporal evolution process of drought-flood abrupt transition events, which is used to solve the technical problem that the existing research on the spatiotemporal evolution of drought-flood abrupt transition events cannot accurately identify the spatiotemporal evolution trend of drought-flood abrupt transition events.

[0006] The first aspect of the present invention provides a method for extracting the spatiotemporal evolution process of drought-flood rapid transition events, comprising:

[0007] In response to the extraction request, the study basin is rasterized, the rasterized basin is determined, and precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month are obtained;

[0008] Determine the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month;

[0009] Using a three-dimensional connected domain analysis method to track and identify the spatiotemporal water and heat of each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, a plurality of aggregated drought and flood events are generated;

[0010] Based on the preset disaster conditions, the spatiotemporal evolution process of each aggregated drought and flood event is extracted to generate an extraction result of the evolution process of the drought and flood sudden transition event.

[0011] Optionally, the precipitation evapotranspiration data includes monthly precipitation and meteorological temperature data; the step of determining the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month includes:

[0012] Calculating the potential evapotranspiration of each grid point in each month according to the meteorological temperature data of each grid point in each month;

[0013] Subtracting the potential evapotranspiration of each grid point in each month from the monthly precipitation, and outputting the climate water balance of each grid point in each month;

[0014] Determining the cumulative distribution function of each of the grid points in each month based on the climate water balance of each of the grid points in each month;

[0015] Normalizing the cumulative distribution function of each grid point in each month to determine the exceedance probability of each grid point in each month;

[0016] The exceedance probability of each grid point in each month is substituted into a preset exponential operation function to calculate the standardized precipitation evapotranspiration index of each grid point in each month.

[0017] Optionally, the step of using a three-dimensional connected domain analysis method to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month to generate a plurality of aggregated drought and flood events includes:

[0018] Determining whether each of the standardized precipitation evapotranspiration indices is within a preset index range;

[0019] According to each month, any grid point corresponding to the standardized precipitation evapotranspiration index that is not within the preset index range is used as the middle grid point of each month;

[0020] Selecting the middle grid point of any month as the target grid point in turn, and taking the middle grid point of any month within the twenty-six neighborhoods of the target grid point as the aggregation grid point;

[0021] Each of the target grid points and a plurality of aggregation grid points corresponding to each of the target grid points are aggregated to generate a plurality of aggregated drought and flood events.

[0022] Optionally, the extraction result of the evolution process of the drought-flood sudden transition event includes the drought-flood sudden transition event, the dry stage of the drought-flood sudden transition event and the flood stage of the drought-flood sudden transition event; the preset disaster conditions include the first disaster condition, the second disaster condition and the third disaster condition; the step of extracting the spatiotemporal evolution process of each of the aggregated drought-flood events based on the preset disaster conditions to generate the extraction result of the evolution process of the drought-flood sudden transition event includes:

[0023] Determining whether each of the aggregated drought and flood events meets the first disaster condition;

[0024] Any aggregated drought or flood event that meets the preset first disaster condition is taken as a target aggregated drought or flood event, and it is determined whether the ratio of the number of drought or flood grids in multiple months of each target aggregated drought or flood event is within a preset ratio range;

[0025] If the ratio of the number of drought and flood grids in multiple months of any target aggregated drought and flood event is within the preset ratio range, the target aggregated drought and flood event will be regarded as a drought-flood sudden transition event;

[0026] Based on the ratio of the number of drought-flood grids in multiple months of each drought-flood sudden transition event, respectively judging whether multiple months corresponding to each drought-flood sudden transition event meet the second disaster condition or the third disaster condition;

[0027] Any month corresponding to any drought-flood transition event that meets the preset second disaster condition is used as the dry period of the drought-flood transition event;

[0028] Any month corresponding to any drought-flood sudden transition event that meets the preset third disaster condition is used as the flood period of the drought-flood sudden transition event.

[0029] Optionally, the meteorological temperature data includes surface net radiation, wind speed at a height of two meters, atmospheric temperature, daily maximum temperature, daily minimum temperature, and dew point temperature; the calculation process of the potential evapotranspiration is specifically as follows:

[0030]

[0031] Where PET is the potential evapotranspiration; Δ is the slope of the saturated water vapor pressure curve; R n is the net radiation of the surface; G is the soil heat flux, which varies with the atmospheric temperature and is calculated based on the long-term step length and atmospheric temperature; r is the hygrometer constant; T is the average temperature at a height of two meters. For standardization, the average temperature at a height of two meters (T) is defined as the maximum daily temperature (T max ) and daily minimum temperature (T min) instead of the average value of the hourly observed temperature; u2 is the wind speed at a height of two meters; es is the saturated water vapor pressure, and the saturated water vapor pressure difference (es-ea) is calculated by the saturated water vapor pressure (es) and the actual water vapor pressure at the dew point temperature (ea); ea is the actual water vapor pressure at the dew point temperature; Cs is the soil heat capacity; T i is the atmospheric temperature at the i-th moment; T i-1 is the atmospheric temperature at the i-1th moment; Δt is the time step; T max is the maximum daily temperature; T min is the daily minimum temperature; e0 is the water vapor pressure at air temperature T; T dew is the dew point temperature.

[0032] Optionally, the preset exponential operation function is specifically:

[0033]

[0034] Wherein, SPEI is the standardized precipitation evapotranspiration index; W is the probability weighted moment; P is the exceedance probability; F(x) is the distribution value corresponding to the cumulative distribution function; f(x) is the probability density function of the Log-logistic distribution of three variables; c0 is the first constant, with a value of 2.515517; c1 is the second constant, with a value of 0.802853; c2 is the third constant, with a value of 0.010328; d1 is the fourth constant, with a value of 1.432788; d2 is the fifth constant, with a value of 0.189269; d3 is the sixth constant, with a value of 0.001308; ln(·) is the natural logarithm function; α is the scale parameter; b is the shape parameter; x is the variable sequence used to formulate the log-logistic distribution, which is D in the present invention. i Sequence, i.e. climate water balance; γ is the starting parameter.

[0035] A second aspect of the present invention provides a device for extracting the spatiotemporal evolution process of drought-flood rapid transition events, comprising:

[0036] A response module, used for responding to the extraction request, rasterizing the research basin, determining the rasterized basin, and obtaining precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month;

[0037] According to the module, it is used to determine the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month;

[0038] A module is used for performing spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month by using a three-dimensional connected domain analysis method to generate multiple aggregated drought and flood events;

[0039] The generation module is used to extract the spatiotemporal evolution process of each aggregated drought and flood event based on preset disaster conditions, and generate an extraction result of the evolution process of drought and flood sudden transition events.

[0040] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for extracting the spatiotemporal evolution process of drought-flood rapid transition events as described in any one of the above items.

[0041] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for extracting the spatiotemporal evolution process of drought-flood rapid transition events as described in any one of the above items.

[0042] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the method for extracting the spatiotemporal evolution process of drought-flood sudden transition events as described in any one of the above items.

[0043] It can be seen from the above technical solutions that the present invention has the following advantages:

[0044] The above technical scheme of the present invention provides a method for extracting the spatiotemporal evolution process of drought-flood sudden change events. When it is necessary to extract the spatiotemporal evolution process of drought-flood sudden change events in a research basin, the research basin is firstly rasterized to determine the rasterized basin, and the precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month are obtained; then, according to the precipitation evapotranspiration data of each grid point in each month, the standardized precipitation evapotranspiration index of each grid point in each month is determined; the three-dimensional connected domain analysis method is used to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, and multiple Aggregate drought and flood events; based on preset disaster conditions, extract the spatiotemporal evolution process of each aggregated drought and flood event, and generate the extraction results of the evolution process of drought and flood sudden transition events; based on the above scheme, use the three-dimensional connected domain analysis method to track and identify the spatiotemporal water and heat of each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, generate multiple aggregated drought and flood events, and extract the spatiotemporal evolution process of each aggregated drought and flood event, and generate the extraction results of the evolution process of drought and flood sudden transition events. The continuity of water and heat connection in three-dimensional space and time during the drought-flood sudden transition process is taken into account, so as to accurately identify the spatiotemporal evolution trend of drought and flood sudden transition events. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 A flowchart of the steps of a method for extracting the spatiotemporal evolution process of drought-flood rapid transition events provided in the first embodiment of the present invention;

[0047] Figure 2 A schematic diagram of spatiotemporal water and heat tracking and identification of drought-flood rapid transition events provided in the first embodiment of the present invention;

[0048] Figure 3 A flowchart of the steps of a method for extracting the spatiotemporal evolution process of drought-flood rapid transition events provided in the second embodiment of the present invention;

[0049] Figure 4 A schematic flow chart of a method for extracting the spatiotemporal evolution process of drought-flood rapid transition events provided in the second embodiment of the present invention;

[0050] Figure 5 This is a structural block diagram of a device for extracting the spatiotemporal evolution process of drought-flood rapid transition events provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0051] The embodiments of the present invention provide a method and device for extracting the spatiotemporal evolution process of drought-flood transition events, which are used to solve the technical problem that the existing research on the spatiotemporal evolution of drought-flood transition events cannot accurately identify the spatiotemporal evolution trend of drought-flood transition events.

[0052] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 , Figure 1 This is a flowchart of the steps of a method for extracting the spatiotemporal evolution process of drought-flood rapid transition events provided in Example 1 of the present invention.

[0054] The present invention provides a method for extracting the spatiotemporal evolution process of drought-flood rapid transition events, comprising:

[0055] Step 101, in response to an extraction request, rasterizing the study basin, determining the rasterized basin, and obtaining precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month.

[0056] It should be noted that after determining the study area (study basin) and performing rasterization processing, the grid-scale precipitation evapotranspiration data of the rasterized basin are collected.

[0057] In this embodiment, in response to the extraction request, the study basin is rasterized, the rasterized basin is determined, and the precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month are obtained.

[0058] Step 102: Determine the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month.

[0059] It should be noted that the Standardized Precipitation Evapotranspiration Index (SPEI) was originally proposed based on the Standardized Rainfall Index. This index not only has the advantage of being able to calculate droughts of various scales like the SPI (Serial Peripheral Interface) index, but also takes into account evapotranspiration, a variable that has a particularly significant impact on drought. Later studies found that this index is very applicable in the identification of drought and the study of its spatiotemporal evolution.

[0060] Specifically, according to the meteorological temperature data of each grid point in each month, the potential evapotranspiration of each grid point in each month is calculated; the potential evapotranspiration of each grid point in each month and the monthly precipitation are subtracted, and the climate water balance of each grid point in each month is output; based on the climate water balance of each grid point in each month, the cumulative distribution function of each grid point in each month is determined; the cumulative distribution function of each grid point in each month is normalized to determine the exceedance probability of each grid point in each month; the exceedance probability of each grid point in each month is substituted into the preset exponential operation function, and the standardized precipitation evapotranspiration index of each grid point in each month is calculated.

[0061] In this embodiment, the standardized precipitation evapotranspiration index of each grid point in each month is determined according to the precipitation evapotranspiration data of each grid point in each month.

[0062] Step 103: Using a three-dimensional connected domain analysis method, the spatiotemporal water and heat tracking and identification of each grid point is performed according to the standardized precipitation evapotranspiration index of each grid point in each month, and a plurality of aggregated drought and flood events are generated.

[0063] The three-dimensional connected domain analysis method is the connected-components-3d (CC3D) algorithm.

[0064] Specifically, it is determined whether each standardized precipitation evapotranspiration index is within the preset index range. According to each month, any grid point corresponding to a standardized precipitation evapotranspiration index that is not within the preset index range is used as the intermediate grid point of each month, and any grid point corresponding to a standardized precipitation evapotranspiration index that is within the preset index range is used as a disaster-free grid point. Assuming that the current number of grid points is 3, each grid point has a corresponding standardized precipitation evapotranspiration index in each month (twelve months), that is, there will be three standardized precipitation evapotranspiration indices for each month. It is determined whether the three standardized precipitation evapotranspiration indices of each month are within the preset index range. If a certain standardized precipitation evapotranspiration index of a certain month is within the preset index range, value, it indicates that the grid point corresponding to the standardized precipitation evapotranspiration index has no disaster in that month, and the grid point corresponding to the standardized precipitation evapotranspiration index is used as a disaster-free grid point. If a certain standardized precipitation evapotranspiration index of a month is not within the preset index range, it indicates that the grid point corresponding to the standardized precipitation evapotranspiration index has a disaster in that month, and the grid point corresponding to the standardized precipitation evapotranspiration index is used as the middle grid point of that month. For example, in February, there are two standardized precipitation evapotranspiration indices that are not within the preset index range, and the grid points corresponding to these two standardized precipitation evapotranspiration indices are used as the middle grid points of February. Based on the above steps and principles, multiple or one middle grid points corresponding to multiple months can be obtained.

[0065] After the above processing, each intermediate grid point will have a corresponding disaster, that is, drought or flood state (drought state and flood state). The preset index range value can be set to [-1,1]. If the standardized precipitation evapotranspiration index is lower than -1 (less than -1), it indicates that the grid point corresponding to the standardized precipitation evapotranspiration index is in a drought state (there is drought disaster) in the corresponding month. If the standardized precipitation evapotranspiration index is higher than 1 (greater than 1), it indicates that the grid point corresponding to the standardized precipitation evapotranspiration index is in a flood state (there is waterlogging disaster) in the corresponding month. The standardized precipitation evapotranspiration index of the intermediate grid point is converted into a binary format to obtain a binary true value corresponding to the standardized precipitation evapotranspiration index of the intermediate grid point. The specific value of the binary true value is 1, indicating that a drought or waterlogging disaster has occurred at the intermediate grid point. The standardized precipitation evapotranspiration index of the disaster-free grid point is converted into a binary format to obtain a binary false value corresponding to the standardized precipitation evapotranspiration index of the intermediate grid point. The specific value of the binary false value is 0, indicating that the disaster-free grid point has not suffered from drought or waterlogging disasters.

[0066] Furthermore, the middle grid points carrying the value 1 in any month are selected in turn as the target grid points, and the middle grid points carrying the value 1 in any month within the twenty-six neighborhoods of the target grid points are selected as aggregation grid points, and each target grid point and the multiple aggregation grid points corresponding to each target grid point are aggregated to generate multiple aggregated drought and flood events; for example, a middle grid point carrying the value 1 in February is selected as the target grid point, and each middle grid point in February and March within the twenty-six neighborhoods of the target grid point that also carries the value 1 is used as an aggregation grid point, so that a target grid point and multiple aggregation grid points in February and multiple aggregation grid points in March can be obtained, and then these grid points are aggregated to obtain aggregated drought and flood events, which are represented by disasters corresponding to multiple grid points in February and disasters corresponding to multiple grid points in March.

[0067] For example, the drought index time series based on the 0.25°×0.25° grid points is arranged into a three-dimensional drought and flood matrix X(lon, lat, t) according to “longitude-latitude-time”, and the size of the matrix is ​​n lon ×n lat ×n t , where n lon is the number of grids in the longitude direction, n lat is the number of grids in the dimension direction, n t is the number of months on the time axis. The position without data is marked as NA and does not participate in the calculation. Based on this three-dimensional drought indicator matrix, drought and flood are identified. The drought and flood status of each grid point in each month is determined by the SPEI index (the standardized precipitation evapotranspiration index of each grid point in each month). Among them, SPEI below -1 is drought, and SPEI above 1 is flood.

[0068] For further information, see Figure 2Based on the SPEI index, the SPEI index stored in the three-dimensional drought and flood matrix (i.e., the dimensions are latitude × longitude × time) is converted into a "0 / 1" binary format. Among them, the "1" voxel indicates that a drought or flood disaster has occurred at the grid point (grid point), and its drought and flood state threshold is set according to the response law of the water and heat supply and demand relationship in the Pearl River Basin to the changing environment (i.e., the preset index range value can be set according to the response law of the water and heat supply and demand relationship in the Pearl River Basin to the changing environment), while the "0" voxel indicates that no disaster has occurred. Then, the "0 / 1" binary array is imported into the three-dimensional connected domain labeling algorithm (three-dimensional connected domain analysis method). Through the connected-components-3d (CC3D) algorithm, compared with other connected domain labeling algorithms, the CC3D algorithm combines the two-pass algorithm and the Array-Based Union-Find to store event sequence information, which is faster for 3D image processing and can directly process non-binary images. Based on the CC3D algorithm, by setting the neighborhood to 26, all possible connected pixels, that is, adjacent pixels with the same value of "1", are searched. That is, the middle grid point with the value of 1 in any month is selected as the target grid point, and the middle grid point with the value of 1 in any month within the 26 neighborhoods of the target grid point is selected as the aggregation grid point. For example, the selection of the 26 neighborhoods considers all adjacent grid points and adjacent days around the central grid unit of the 3×3×3 cube, that is, 3×3=9 grids of the previous day, 3×3-1=8 grids excluding the central grid of the day, and 3×3=9 grids of the next day. By continuously marking each "1" pixel, the "1" pixels are connected and aggregated into a 3D structure indicating a single continuous event to jointly track the continuous drought and flood status in space and time, and finally a series of spatiotemporal aggregated drought and flood events, that is, multiple aggregated drought and flood events, are obtained. The efficient three-dimensional connected domain labeling algorithm can effectively identify the continuous drought and flood events that occur on the grid in the current period, move to the adjacent grid in the next period, and ensure that different drought and flood events do not overlap or touch in time and space, that is, different drought and flood events are neither adjacent in space nor continuous in time. Next, the characteristics of drought and flood periods in spatiotemporal aggregate drought and flood events are analyzed in turn, and spatiotemporal aggregate drought and flood events with drought and flood conversion in time are screened out to realize the extraction of the three-dimensional process of drought and flood conversion events.

[0069] In this embodiment, a three-dimensional connected domain analysis method is used to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, and a plurality of aggregated drought and flood events are generated.

[0070] Step 104: Based on the preset disaster conditions, extract the spatiotemporal evolution process of each aggregated drought and flood event to generate an extraction result of the drought and flood sudden transition event evolution process.

[0071] The extracted results of the evolution process of drought-flood sudden transition events include drought-flood sudden transition events, the dry phase of drought-flood sudden transition events and the flood phase of drought-flood sudden transition events.

[0072] The preset disaster conditions include the first disaster conditions, the second disaster conditions and the third disaster conditions.

[0073] It should be noted that according to the number of spatiotemporal continuous events, the monthly drought-flood grid number ratio and the drought-flood intensity ratio of each event are analyzed in turn. That is, based on the preset disaster conditions, the spatiotemporal evolution process of each aggregated drought-flood event is extracted to generate the extraction results of the drought-flood abrupt transition event evolution process.

[0074] Specifically, numbered events with both drought and flood disasters and whose drought-flood grid number ratio is within the preset ratio range are selected and identified as drought-flood sudden transition events, that is, whether each aggregated drought-flood event meets the first disaster condition is judged separately; when any aggregated drought-flood event meets the preset first disaster condition, whether the drought-flood grid number ratio of multiple months of the aggregated drought-flood event is within the preset ratio range is judged separately; if the drought-flood grid number ratio of multiple months of the aggregated drought-flood event is within the preset ratio range, the aggregated drought-flood event is regarded as a drought-flood sudden transition event; wherein, the aggregated drought-flood event is composed of a target grid point and multiple aggregated grid points corresponding to the target grid point, and the target grid point and the aggregated grid point are represented as There is a drought or a waterlogging disaster in a certain month, so the aggregated drought and waterlogging event is represented by the existence of multiple or one droughts and / or multiple or one waterlogging disasters in multiple months; the first disaster condition is that the aggregated drought and waterlogging event has both drought and waterlogging disasters, that is, if there is a drought at a certain grid point (target grid point or aggregated grid point) in any month constituting the aggregated drought and waterlogging event and there is a waterlogging disaster at another grid point (target grid point or aggregated grid point) in any month, it indicates that the aggregated drought and waterlogging event meets the preset first disaster condition and is used as the target aggregated drought and waterlogging event; on the contrary, if there is only drought or waterlogging at each grid point in multiple months constituting the aggregated drought and waterlogging event, it indicates that the aggregated drought and waterlogging event does not meet the preset first disaster condition and is eliminated.

[0075] Based on the above foundation, after obtaining multiple target aggregated drought and flood events, it is judged whether the ratio of the number of drought and flood grids in multiple months of each target aggregated drought and flood event is within the preset ratio range value; if the ratio of the number of drought and flood grids in multiple months of any target aggregated drought and flood event is within the preset ratio range value, the target aggregated drought and flood event is regarded as a drought-flood sudden transition event; if the ratio of the number of drought and flood grids in multiple months of any target aggregated drought and flood event is not within the preset ratio range value or one of the ratios of the number of drought and flood grids in multiple months of any target aggregated drought and flood event is not within the preset ratio range value, the target aggregated drought and flood event is eliminated; wherein, the preset ratio range value can be set as needed, for example, in the range of 0.1 to 10, and the present invention is not limited thereto.

[0076] Furthermore, after obtaining multiple drought-flood transition events, based on the ratio of the number of drought-flood grids in multiple months of each drought-flood transition event, it is judged whether the multiple months corresponding to each drought-flood transition event meet the second disaster condition or the third disaster condition; wherein, the second disaster condition is that if the disasters of each grid point in a certain month of the drought-flood transition event are all droughts or the ratio of the number of drought-flood grids in this month is less than a preset first threshold, then this month is regarded as the drought period of the drought-flood transition event; if the disasters of each grid point in a certain month of the drought-flood transition event are not all droughts or the ratio of the number of drought-flood grids in this month is greater than or equal to the preset first threshold, then this month is regarded as the transition period of the drought-flood transition event; wherein the preset first threshold can be set as needed, for example 0.05, and the present invention is not limited to this.

[0077] Based on the above foundation, the third disaster condition is that if the disasters of all grid points in a certain month of the drought-flood sudden transition event are all floods or the ratio of the number of drought-flood grids in that month is greater than the preset second threshold, then this month will be regarded as the flood stage of the drought-flood sudden transition event; if the disasters of all grid points in a certain month of the drought-flood sudden transition event are not all floods or the ratio of the number of drought-flood grids in that month is less than or equal to the preset second threshold, then this month will be regarded as the transition period of the drought-flood sudden transition event; the preset second threshold can be set as needed, for example 20, and the present invention is not limited to this.

[0078] In this embodiment, based on preset disaster conditions, the spatiotemporal evolution process of each aggregated drought and flood event is extracted to generate an extraction result of the evolution process of drought and flood sudden transition events.

[0079] In an embodiment of the present invention, the present invention provides a method for extracting the spatiotemporal evolution process of drought-flood abrupt transition events. When it is necessary to extract the spatiotemporal evolution process of drought-flood abrupt transition events in a research basin, the research basin is firstly rasterized to determine the rasterized basin, and the precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month are obtained; then, according to the precipitation evapotranspiration data of each grid point in each month, the standardized precipitation evapotranspiration index of each grid point in each month is determined; a three-dimensional connected domain analysis method is used to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, and multiple grid points are generated. Aggregated drought and flood events; based on the preset disaster conditions, the spatiotemporal evolution process of each aggregated drought and flood event is extracted to generate the extraction results of the evolution process of drought-flood sudden transition events; based on the above scheme, the three-dimensional connected domain analysis method is used to track and identify the spatiotemporal water and heat of each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, to generate multiple aggregated drought and flood events, and the spatiotemporal evolution process of each aggregated drought and flood event is extracted to generate the extraction results of the evolution process of drought-flood sudden transition events. The continuity of the water and heat connection in three-dimensional space and time during the drought-flood sudden transition process is taken into account, so as to accurately identify the spatiotemporal evolution trend of drought-flood sudden transition events.

[0080] See also Figure 3 , Figure 3 This is a flowchart of the steps of a method for extracting the spatiotemporal evolution process of drought-flood sudden change events provided in Example 2 of the present invention.

[0081] The present invention provides a method for extracting the spatiotemporal evolution process of drought-flood rapid transition events, comprising:

[0082] Step 301, in response to an extraction request, rasterizing the study basin, determining the rasterized basin, and obtaining precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month.

[0083] In this embodiment, in response to the extraction request, the study basin is gridded, the gridded basin is determined, and the precipitation evapotranspiration data of multiple grid points in the gridded basin in each month are obtained.

[0084] Step 302: Determine the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month.

[0085] Precipitation evapotranspiration data include monthly precipitation and meteorological temperature data; among them, meteorological temperature data include surface net radiation, wind speed at two meters high, atmospheric temperature, daily maximum temperature, daily minimum temperature, and dew point temperature.

[0086] Further, step 302 may include the following sub-steps:

[0087] S21, calculating the potential evapotranspiration of each grid point in each month according to the meteorological temperature data of each grid point in each month;

[0088] It should be noted that the Penman-Monteith formula of FAO 56 is usually used to calculate the potential evapotranspiration of the basin. Specifically, the potential evapotranspiration of each grid point in each month can be calculated based on the meteorological temperature data of each grid point in each month. The calculation process of the potential evapotranspiration is as follows:

[0089]

[0090] Where PET is the potential evapotranspiration; Δ is the slope of the saturated water vapor pressure curve; R n is the net radiation of the surface; G is the soil heat flux, which varies with the atmospheric temperature and is calculated based on the long-term step length and atmospheric temperature; r is the hygrometer constant; T is the average temperature at a height of 2 meters. For standardization, the average temperature at a height of 2 meters (T) is defined as the maximum daily temperature (T max ) and daily minimum temperature (T min ) instead of the average of hourly observed temperatures; u2 is the wind speed at a height of 2 meters; es is the saturated vapor pressure, and the saturated vapor pressure difference (es-ea) is calculated from the saturated vapor pressure (es) and the actual vapor pressure at the dew point temperature (ea); ea is the actual vapor pressure at the dew point temperature; Cs is the soil heat capacity; T i is the atmospheric temperature at the i-th moment; T i-1 is the atmospheric temperature at the i-1th moment; Δt is the time step; T max is the maximum daily temperature; T min is the daily minimum temperature; e0 is the water vapor pressure at air temperature T; T dew is the dew point temperature.

[0091] S22, performing subtraction operation on the potential evapotranspiration of each grid point in each month and the monthly precipitation, and outputting the climate water balance of each grid point in each month;

[0092] It should be noted that the potential evapotranspiration of each grid point in each month is subtracted from the monthly precipitation, that is, the rainfall minus the evapotranspiration, so as to obtain the climate water balance of each grid point in each month. The processing process of climate water balance can be expressed as:

[0093] D i =P i -PET i ;

[0094] Among them, D i is the climate water balance of the ith month (original data series); P iis the climate water balance in the ith month; PET i is the potential evapotranspiration (potential evapotranspiration data) of the i-th month.

[0095] It is worth mentioning that for the climate water balance at other time scales, it can be expressed as:

[0096]

[0097] in, is the nth climate water balance; k is the calculation time scale of the SPEI index; P n-i is the standard precipitation in the ni month; PET n-i is the ni-th potential evapotranspiration; i is the i-th month of calculation.

[0098] S23, based on the climate water balance of each grid point in each month, determining the cumulative distribution function of each grid point in each month;

[0099] S24, performing normalization on the cumulative distribution function of each grid point in each month to determine the exceedance probability of each grid point in each month;

[0100] S25, substituting the exceedance probability of each grid point in each month into the preset exponential operation function, and calculating the standardized precipitation evapotranspiration index of each grid point in each month.

[0101] It should be noted that climate water balance is the core of the SPEI index. The climate water balance in continuous time forms a water balance sequence D, and then the water balance sequence is normalized and fitted using the three-variable Log-logistic distribution to obtain the cumulative distribution function F(X) about D. The three-variable Log-logistic distribution has the best fitting effect on the water balance sequence, so it is used for the normalization of water balance.

[0102] Furthermore, the Log-Logistic distribution function is standardized and the SPEI index is calculated, that is, the cumulative distribution function of each grid point in each month is normalized, the exceedance probability of each grid point in each month is determined, and the SPEI index is calculated; wherein the cumulative distribution function can be expressed as:

[0103]

[0104] Among them, F(x) is the distribution value corresponding to the cumulative distribution function; f(x) is the probability density function of the Log-logistic distribution of three variables; W0 is the original data sequence D i The first probability weighted moment after the ascending order of; W1 is the original data sequence D iThe second probability weighted moment after the ascending order of; W2 is the original data sequence D i The third probability weighted moment after the ascending order of ; Γ(b) is the gamma function; α is the scale parameter, and the scale parameter, shape parameter, and starting parameter can all be obtained according to the parameter estimation method; b is the shape parameter; x is the variable sequence used to formulate the log-logistic distribution, which is D in the present invention. i Sequence, i.e. climate water balance; γ is the starting parameter.

[0105] Furthermore, the exponential operation function is preset, specifically:

[0106]

[0107] Wherein, SPEI is the standardized precipitation evapotranspiration index; W is the probability weighted moment; P is the exceedance probability; F(x) is the distribution value corresponding to the cumulative distribution function; f(x) is the probability density function of the Log-logistic distribution of three variables; c0 is the first constant, with a value of 2.515517; c1 is the second constant, with a value of 0.802853; c2 is the third constant, with a value of 0.010328; d1 is the fourth constant, with a value of 1.432788; d2 is the fifth constant, with a value of 0.189269; d3 is the sixth constant, with a value of 0.001308; ln(·) is the natural logarithm function; α is the scale parameter; b is the shape parameter; x is the variable sequence used to formulate the log-logistic distribution, which is D in the present invention. i Sequence, i.e. climate water balance; γ is the starting parameter.

[0108] In this embodiment, the standardized precipitation evapotranspiration index of each grid point in each month is determined according to the precipitation evapotranspiration data of each grid point in each month.

[0109] Step 303: determine whether each standardized precipitation evapotranspiration index is within a preset index range.

[0110] In this embodiment, it is determined whether each standardized precipitation evapotranspiration index is within a preset index range.

[0111] Step 304: According to each month, any grid point corresponding to the standardized precipitation evapotranspiration index that is not within the preset index range is used as the middle grid point of each month.

[0112] In this embodiment, according to each month, any grid point corresponding to the standardized precipitation evapotranspiration index that is not within the preset index range is used as the middle grid point of each month.

[0113] Step 305: sequentially select the middle grid points of any month as target grid points, and select the middle grid points of any month within the twenty-six neighborhoods of the target grid points as aggregated grid points.

[0114] In this embodiment, the middle grid points of any month are selected in sequence as target grid points, and the middle grid points of any month within the twenty-six neighborhoods of the target grid points are selected as aggregation grid points.

[0115] Step 306: Aggregate each target grid point and the multiple aggregation grid points corresponding to each target grid point to generate multiple aggregated drought and flood events.

[0116] In this embodiment, each target grid point and a plurality of aggregation grid points corresponding to each target grid point are aggregated to generate a plurality of aggregated drought and flood events.

[0117] Step 307: Based on the preset disaster conditions, extract the spatiotemporal evolution process of each aggregated drought and flood event to generate an extraction result of the drought and flood sudden transition event evolution process.

[0118] The extracted results of the evolution process of drought-flood sudden transition events include drought-flood sudden transition events, the dry stages of drought-flood sudden transition events and the flood stages of drought-flood sudden transition events; the preset disaster conditions include the first disaster conditions, the second disaster conditions and the third disaster conditions.

[0119] Further, step 307 may include the following sub-steps:

[0120] S71, respectively judging whether each aggregated drought and flood event meets the first disaster condition;

[0121] S72, taking any aggregated drought or flood event that meets the preset first disaster condition as a target aggregated drought or flood event, and determining whether the ratio of the number of drought or flood grids in multiple months of each target aggregated drought or flood event is within a preset ratio range;

[0122] S73, if the ratios of the number of drought and flood grids in multiple months of any target aggregated drought and flood event are all within the preset ratio range, the target aggregated drought and flood event is regarded as a drought-flood sudden transition event;

[0123] S74, based on the ratio of the number of drought-flood grids in multiple months of each drought-flood sudden transition event, respectively determine whether multiple months corresponding to each drought-flood sudden transition event meet the second disaster condition or the third disaster condition;

[0124] S75, taking any month corresponding to any drought-flood sudden transition event that meets the preset second disaster condition as the dry period of the drought-flood sudden transition event;

[0125] S76. Any month corresponding to any drought-flood transition event that meets the preset third disaster condition is used as the flood period of the drought-flood transition event.

[0126] In this embodiment, based on preset disaster conditions, the spatiotemporal evolution process of each aggregated drought and flood event is extracted to generate an extraction result of the evolution process of drought and flood sudden transition events.

[0127] As a comparison of technical effects, it can be combined with existing technologies for reference. At present, the research on drought-flood transition events focuses on the evolution characteristics of drought-flood state in time series, and rarely considers the continuity of water-heat connection in three-dimensional space-time during drought-flood transition. There are difficulties in accurately identifying and predicting the spatiotemporal co-evolution trend of drought-flood transition events in complex land-sea water-heat interaction areas. Therefore, it is necessary to develop a short-period drought-flood transition theory and an efficient connected domain marking algorithm for drought-flood state, establish a three-dimensional identification method for drought-flood transition process considering the water-heat balance and water-heat spatiotemporal connection of the basin, and improve the identification and prediction accuracy of drought-flood transition process under changing environment. It can innovate the research paradigm of drought-flood transition process evolution analysis, enrich the theoretical system of drought-flood transition process research in the basin, and provide scientific methods and data support for the prevention and control of drought-flood transition compound disasters in the basin.

[0128] For the above questions, please refer to Figure 4 The present invention proposes a method for extracting the spatiotemporal evolution process of drought-flood sudden transition events. First, the basin meteorological data (precipitation evapotranspiration data), namely, precipitation, wind speed, radiation and other meteorological data, is collected. That is, the study area is determined, and the monthly precipitation and potential evapotranspiration data in the basin are collected. Based on the precipitation data of the basin, the standardized precipitation evapotranspiration SPEI index is calculated to identify the drought and flood status of each grid point in the basin in each month, and then the 3D connected domain algorithm is used to track the evolution trajectory of extreme drought and flood disasters in the basin; the 3D connected domain algorithm is then used to track the evolution trajectory of extreme drought and flood disasters in the basin, that is, the three-dimensional connected domain analysis method is used to track and identify the spatiotemporal water and heat of each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, and multiple aggregated drought and flood events are generated. Finally, the spatiotemporal evolution process of drought-flood sudden transition events is extracted, which can provide a scientific method for tracking and early warning of drought and flood disaster processes in the basin, and provide a scientific basis for the prevention and control of drought and flood disasters in the basin.

[0129] In an embodiment of the present invention, the present invention provides a method for extracting the spatiotemporal evolution process of drought-flood abrupt transition events. When it is necessary to extract the spatiotemporal evolution process of drought-flood abrupt transition events in a research basin, the research basin is firstly rasterized to determine the rasterized basin, and the precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month are obtained; then, according to the precipitation evapotranspiration data of each grid point in each month, the standardized precipitation evapotranspiration index of each grid point in each month is determined; a three-dimensional connected domain analysis method is used to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, and multiple grid points are generated. Aggregated drought and flood events; based on the preset disaster conditions, the spatiotemporal evolution process of each aggregated drought and flood event is extracted to generate the extraction results of the evolution process of drought-flood sudden transition events; based on the above scheme, the three-dimensional connected domain analysis method is used to track and identify the spatiotemporal water and heat of each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, to generate multiple aggregated drought and flood events, and the spatiotemporal evolution process of each aggregated drought and flood event is extracted to generate the extraction results of the evolution process of drought-flood sudden transition events. The continuity of the water and heat connection in three-dimensional space and time during the drought-flood sudden transition process is taken into account, so as to accurately identify the spatiotemporal evolution trend of drought-flood sudden transition events.

[0130] See also Figure 5 , Figure 5 This is a structural block diagram of a device for extracting the spatiotemporal evolution process of drought-flood rapid transition events provided in Example 3 of the present invention.

[0131] The present invention provides a device for extracting the spatiotemporal evolution process of drought-flood rapid transition events, comprising:

[0132] The response module 501 is used to respond to the extraction request, rasterize the study basin, determine the rasterized basin, and obtain the precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month;

[0133] According to module 502, for determining the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month;

[0134] Module 503 is used to use a three-dimensional connected domain analysis method to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, and generate multiple aggregated drought and flood events;

[0135] The generation module 504 is used to extract the spatiotemporal evolution process of each aggregated drought and flood event based on preset disaster conditions, and generate an extraction result of the evolution process of drought and flood sudden transition events.

[0136] Further, the precipitation evapotranspiration data includes monthly precipitation and meteorological temperature data; according to module 502, it is specifically used for:

[0137] According to the meteorological temperature data of each grid point in each month, the potential evapotranspiration of each grid point in each month is calculated;

[0138] Subtract the potential evapotranspiration and monthly precipitation of each grid point in each month, and output the climate water balance of each grid point in each month;

[0139] Based on the climate water balance of each grid point in each month, the cumulative distribution function of each grid point in each month is determined;

[0140] Normalize the cumulative distribution function of each grid point in each month to determine the exceedance probability of each grid point in each month;

[0141] The exceedance probability of each grid point in each month is substituted into the preset exponential operation function to calculate the standardized precipitation evapotranspiration index of each grid point in each month.

[0142] Further, module 503 is used to:

[0143] Determine whether each standardized precipitation evapotranspiration index is within the preset index range;

[0144] According to each month, any grid point corresponding to the standardized precipitation evapotranspiration index that is not within the preset index range is used as the middle grid point of each month;

[0145] The middle grid point of any month is selected as the target grid point in turn, and the middle grid point of any month within the 26 neighborhoods of the target grid point is selected as the aggregation grid point;

[0146] Each target grid point and multiple aggregation grid points corresponding to each target grid point are aggregated to generate multiple aggregated drought and flood events.

[0147] Further, the extraction result of the drought-flood transition event evolution process includes the drought-flood transition event, the dry period of the drought-flood transition event and the flood period of the drought-flood transition event; the preset disaster conditions include the first disaster condition, the second disaster condition and the third disaster condition; the generation module 504 is specifically used for:

[0148] Determine whether each aggregated drought and flood event meets the first disaster condition;

[0149] Any aggregated drought or flood event that meets the preset first disaster condition is taken as the target aggregated drought or flood event, and it is determined whether the ratio of the number of drought or flood grids in multiple months of each target aggregated drought or flood event is within the preset ratio range;

[0150] If the ratio of the number of drought and flood grids in multiple months of any target aggregated drought and flood event is within the preset ratio range, the target aggregated drought and flood event will be regarded as a drought-flood sudden transition event;

[0151] Based on the ratio of the number of drought-flood grids in multiple months of each drought-flood sudden transition event, it is judged whether the multiple months corresponding to each drought-flood sudden transition event meet the second disaster condition or the third disaster condition;

[0152] Any month corresponding to any drought-flood sudden change event that meets the preset second disaster condition is regarded as the dry period of the drought-flood sudden change event;

[0153] Any month corresponding to any drought-flood sudden transition event that meets the preset third disaster condition is taken as the flood period of the drought-flood sudden transition event.

[0154] Furthermore, the meteorological temperature data include surface net radiation, wind speed at two meters high, atmospheric temperature, daily maximum temperature, daily minimum temperature, and dew point temperature; the calculation process of potential evapotranspiration is as follows:

[0155]

[0156] Where PET is the potential evapotranspiration; Δ is the slope of the saturated water vapor pressure curve; R n is the net radiation of the surface; G is the soil heat flux, which varies with the atmospheric temperature and is calculated based on the long-term step length and atmospheric temperature; r is the hygrometer constant; T is the average temperature at a height of two meters. For standardization, the average temperature at a height of two meters (T) is defined as the maximum daily temperature (T max ) and daily minimum temperature (T min ) instead of the average value of the hourly observed temperature; u2 is the wind speed at a height of two meters; es is the saturated water vapor pressure, and the saturated water vapor pressure difference (es-ea) is calculated by the saturated water vapor pressure (es) and the actual water vapor pressure at the dew point temperature (ea); ea is the actual water vapor pressure at the dew point temperature; Cs is the soil heat capacity; T i is the atmospheric temperature at the i-th moment; T i-1 is the atmospheric temperature at the i-1th moment; Δt is the time step; T max is the maximum daily temperature; T min is the daily minimum temperature; e0 is the water vapor pressure at air temperature T; T dew is the dew point temperature.

[0157] Furthermore, the exponential operation function is preset, specifically:

[0158]

[0159] Wherein, SPEI is the standardized precipitation evapotranspiration index; W is the probability weighted moment; P is the exceedance probability; F(x) is the distribution value corresponding to the cumulative distribution function; f(x) is the probability density function of the Log-logistic distribution of three variables; c0 is the first constant, with a value of 2.515517; c1 is the second constant, with a value of 0.802853; c2 is the third constant, with a value of 0.010328; d1 is the fourth constant, with a value of 1.432788; d2 is the fifth constant, with a value of 0.189269; d3 is the sixth constant, with a value of 0.001308; ln(·) is the natural logarithm function; α is the scale parameter; b is the shape parameter; x is the variable sequence used to formulate the log-logistic distribution, which is D in the present invention. i Sequence, i.e. climate water balance; γ is the starting parameter.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] Embodiment 4 of the present invention provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the method for extracting the spatiotemporal evolution process of drought-flood sudden transition events as in any of the above embodiments.

[0162] Embodiment 5 of the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method for extracting the spatiotemporal evolution process of drought-flood rapid transition events as in any of the above embodiments are implemented.

[0163] Embodiment 6 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for extracting the spatiotemporal evolution process of drought-flood sudden transition events as in any of the above embodiments.

[0164] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for extracting the spatiotemporal evolution process of drought-flood rapid transition events, characterized in that: include: In response to the extraction request, the study basin is rasterized, the rasterized basin is determined, and precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month are obtained; Determine the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month; Using a three-dimensional connected domain analysis method to track and identify the spatiotemporal water and heat of each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month, a plurality of aggregated drought and flood events are generated; Based on the preset disaster conditions, extract the spatiotemporal evolution process of each aggregated drought and flood event to generate an extraction result of the drought and flood sudden transition event evolution process; The extraction result of the evolution process of the drought-flood sudden transition event includes the drought-flood sudden transition event, the dry stage of the drought-flood sudden transition event and the flood stage of the drought-flood sudden transition event; the preset disaster conditions include the first disaster condition, the second disaster condition and the third disaster condition; the step of extracting the spatiotemporal evolution process of each of the aggregated drought-flood events based on the preset disaster conditions to generate the extraction result of the evolution process of the drought-flood sudden transition event includes: Determining whether each of the aggregated drought and flood events meets the first disaster condition; Taking any aggregated drought or flood event that meets the first disaster condition as a target aggregated drought or flood event, and determining whether the ratio of the number of drought or flood grids in multiple months of each target aggregated drought or flood event is within a preset ratio range; If the ratio of the number of drought and flood grids in multiple months of any target aggregated drought and flood event is within the preset ratio range, the target aggregated drought and flood event will be regarded as a drought-flood sudden transition event; Based on the ratio of the number of drought-flood grids in multiple months of each drought-flood sudden transition event, respectively judging whether multiple months corresponding to each drought-flood sudden transition event meet the second disaster condition or the third disaster condition; Any month corresponding to any drought-flood transition event that meets the second disaster condition is used as the dry period of the drought-flood transition event; Any month corresponding to any drought-flood sudden transition event that meets the third disaster condition is used as the flood period of the drought-flood sudden transition event.

2. The method for extracting the spatiotemporal evolution process of drought-flood rapid transition events according to claim 1 is characterized in that: The precipitation evapotranspiration data includes monthly precipitation and meteorological temperature data; the step of determining the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month includes: Calculating the potential evapotranspiration of each grid point in each month according to the meteorological temperature data of each grid point in each month; Subtracting the potential evapotranspiration of each grid point in each month from the monthly precipitation, and outputting the climate water balance of each grid point in each month; Determining the cumulative distribution function of each of the grid points in each month based on the climate water balance of each of the grid points in each month; Normalizing the cumulative distribution function of each grid point in each month to determine the exceedance probability of each grid point in each month; The exceedance probability of each grid point in each month is substituted into a preset exponential operation function to calculate the standardized precipitation evapotranspiration index of each grid point in each month.

3. The method for extracting the spatiotemporal evolution process of drought-flood rapid transition events according to claim 1 is characterized in that: The step of using the three-dimensional connected domain analysis method to perform spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month to generate multiple aggregated drought and flood events includes: Determining whether each of the standardized precipitation evapotranspiration indices is within a preset index range; According to each month, any grid point corresponding to the standardized precipitation evapotranspiration index that is not within the preset index range is used as the middle grid point of each month; Selecting the middle grid point of any month as the target grid point in turn, and taking the middle grid point of any month within the twenty-six neighborhoods of the target grid point as the aggregation grid point; Each of the target grid points and a plurality of aggregation grid points corresponding to each of the target grid points are aggregated to generate a plurality of aggregated drought and flood events.

4. The method for extracting the spatiotemporal evolution of drought-flood rapid transition events according to claim 2, characterized in that: The meteorological temperature data include surface net radiation, wind speed at a height of two meters, atmospheric temperature, daily maximum temperature, daily minimum temperature, and dew point temperature; the calculation process of the potential evapotranspiration is specifically as follows: ； Among them, PET is potential evaporative emission; is the slope of the saturated water vapor pressure curve; is the net radiation of the surface; G is the soil heat flux, which varies with the atmospheric temperature and is calculated based on the long-term step length and atmospheric temperature; r is the hygrometer constant; is the maximum daily temperature; is the daily minimum temperature; T is the average temperature at a height of two meters. For standardization, the average temperature at a height of two meters is defined as the average of the daily maximum temperature and the daily minimum temperature, rather than the average of the hourly observed temperature; u2 is the wind speed at a height of two meters; es is the saturated water vapor pressure, ea is the actual water vapor pressure at the dew point temperature, and es-ea is the saturated water vapor pressure difference, which is calculated from the saturated water vapor pressure and the actual water vapor pressure at the dew point temperature; Cs is the soil heat capacity; is the atmospheric temperature at the i-th moment; is the atmospheric temperature at the i-1th moment; is the time step; e0 is the water vapor pressure at air temperature T; is the dew point temperature.

5. The method for extracting the spatiotemporal evolution process of drought-flood rapid transition events according to claim 2 is characterized in that: The preset exponential operation function is specifically: ； Among them, SPEI is the standardized precipitation evapotranspiration index; W is the probability weighted moment; P is the exceedance probability; F(x) is the distribution value corresponding to the cumulative distribution function; f(x) is the probability density function of the Log-logistic distribution of the three variables; is the first constant, and its value is 2.515517; is the second constant, and its value is 0.802853; is the third constant, and its value is 0.010328; is the fourth constant, and its value is 1.432788; is the fifth constant, and its value is 0.189269; is the sixth constant, and its value is 0.001308; is the natural logarithm function; is the scale parameter; is the shape parameter; x is the variable sequence used to formulate the log-logistic distribution, corresponding to D i sequence, i.e., climate water balance; is the starting parameter.

6. A device for extracting the spatiotemporal evolution process of drought-flood rapid transition events, characterized in that: include: A response module, used for responding to the extraction request, rasterizing the research basin, determining the rasterized basin, and obtaining precipitation evapotranspiration data of multiple grid points in the rasterized basin in each month; According to the module, it is used to determine the standardized precipitation evapotranspiration index of each grid point in each month according to the precipitation evapotranspiration data of each grid point in each month; A module is used for performing spatiotemporal water and heat tracking and identification on each grid point according to the standardized precipitation evapotranspiration index of each grid point in each month by using a three-dimensional connected domain analysis method to generate multiple aggregated drought and flood events; A generation module, for extracting the spatiotemporal evolution process of each aggregated drought and flood event based on preset disaster conditions, and generating an extraction result of the evolution process of drought and flood sudden transition events; The extraction result of the drought-flood transition event evolution process includes the drought-flood transition event, the dry period of the drought-flood transition event and the flood period of the drought-flood transition event; the preset disaster conditions include the first disaster condition, the second disaster condition and the third disaster condition; the generation module is specifically used to: Determining whether each of the aggregated drought and flood events meets the first disaster condition; Taking any aggregated drought or flood event that meets the first disaster condition as a target aggregated drought or flood event, and determining whether the ratio of the number of drought or flood grids in multiple months of each target aggregated drought or flood event is within a preset ratio range; If the ratio of the number of drought and flood grids in multiple months of any target aggregated drought and flood event is within the preset ratio range, the target aggregated drought and flood event will be regarded as a drought-flood sudden transition event; Based on the ratio of the number of drought-flood grids in multiple months of each drought-flood sudden transition event, respectively judging whether multiple months corresponding to each drought-flood sudden transition event meet the second disaster condition or the third disaster condition; Any month corresponding to any drought-flood transition event that meets the second disaster condition is used as the dry period of the drought-flood transition event; Any month corresponding to any drought-flood sudden transition event that meets the third disaster condition is used as the flood period of the drought-flood sudden transition event.

7. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for extracting the spatiotemporal evolution process of drought-flood rapid transition events as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for extracting the spatiotemporal evolution process of drought-flood rapid transition events as described in any one of claims 1-5 is implemented.

9. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the method for extracting the spatiotemporal evolution process of drought-flood sudden transition events as described in any one of claims 1-5.

Images