A method and device for quantifying the risk of drought-flood sudden change event process

Abstract

The invention discloses a method and device for quantifying the risk of drought-flood sudden transition event process. The invention comprises the following steps: obtaining precipitation evapotranspiration data of multiple grid points in a gridded watershed in each month; 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; determining multiple target months and multiple target drought-flood patches based on the standardized precipitation evapotranspiration index of each grid point in each month and each grid point; determining multiple drought-flood sudden transition events according to each target month and each target drought-flood patch, performing three-dimensional drought-flood feature extraction on each drought-flood sudden transition event, and outputting multiple characteristic attributes of each drought-flood sudden transition event; generating risk quantification results of each drought-flood sudden transition event according to the multiple characteristic attributes of each drought-flood sudden transition event; and solving the technical problem that the existing analysis method of drought-flood sudden transition events cannot comprehensively reflect the overall risk situation of drought-flood sudden transition disasters to water resources security in the watershed.

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 quantifying the risk of drought-flood rapid transition events. Background Art

[0002] A drought-flood transition event is a complex disaster in which drought and floods occur alternately. Specifically, a region or river basin experiences continuous drought in the early stage, followed by concentrated heavy rainfall in the later stage, which causes rivers to surge and cities to be flooded. In a short period of time, the drought state turns into a flood state, reflecting the coexistence of drought and flood extreme events in a short period of time. It has attracted widespread research attention in recent years and has become a research hotspot for regional complex disaster risks.

[0003] Since drought-flood transition events and their characteristic changes are discontinuous in time and space, frequency analysis of the characteristics of drought-flood transition events is helpful to formulate flood control and drought relief measures and plans, and provide a theoretical basis for the prevention of drought-flood transition disasters in areas with unbalanced temporal and spatial distribution of water resources. Analyzing the changing trends of the frequency, intensity, duration and affected area of ​​the evolution process of drought-flood transition events and accurately quantifying the impact of drought-flood transition events are of great significance to ensuring the security of water resources in the basin.

[0004] Most of the existing analysis methods for drought-flood transition events identify drought-flood transition events through existing indicators such as the drought-flood transition index, or focus on qualitative or semi-quantitative methods to analyze and identify single factors or single indicators. However, the above methods lack analysis of the comprehensive impact degree, resulting in the inability to comprehensively reflect the overall risk situation of drought-flood transition disasters to water resources security in the basin. Summary of the invention

[0005] The present invention provides a method and device for quantifying the risk of drought-flood sudden transition events, which are used to solve the technical problem that the existing analysis method of drought-flood sudden transition events cannot comprehensively reflect the overall risk situation of drought-flood sudden transition disasters to water resources security in a river basin.

[0006] A first aspect of the present invention provides a method for quantifying the risk of a drought-flood transition event process, comprising:

[0007] In response to the quantification 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] Based on the standardized precipitation evapotranspiration index of each grid point in each month, drought and flood patches are extracted for each grid point to determine multiple target months and multiple target drought and flood patches;

[0010] According to each of the target months and each of the target drought-flood patches, a plurality of drought-flood abrupt transition events are determined, and three-dimensional drought-flood feature extraction is performed on each of the drought-flood abrupt transition events using the run theory, and a plurality of characteristic attributes of each of the drought-flood abrupt transition events are output;

[0011] According to a plurality of characteristic attributes of each of the drought-flood transition events, a risk quantification result of each of the drought-flood transition events is generated.

[0012] 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:

[0013] Calculating the potential evapotranspiration of each grid point in each month according to the meteorological temperature data of each grid point in each month;

[0014] 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;

[0015] 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;

[0016] Normalizing the cumulative distribution function of each grid point in each month to determine the exceedance probability of each grid point in each month;

[0017] 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.

[0018] Optionally, the step of extracting drought and waterlogging patches from each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and determining a plurality of target months and a plurality of target drought and waterlogging patches comprises:

[0019] Determining whether each of the standardized precipitation evapotranspiration indices is within a preset index range;

[0020] 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;

[0021] Based on the drought and flood status of each of the intermediate grid points, the spatial clustering method is used to perform spatiotemporal clustering on each of the intermediate grid points according to the month to determine a plurality of intermediate drought and flood patches;

[0022] Respectively comparing the area of ​​each intermediate drought and flood patch with a preset area threshold;

[0023] Any intermediate drought or flood patch that is larger than the preset area threshold is taken as a target drought or flood patch, and the month in which the target drought or flood patch exists is taken as a target month.

[0024] Optionally, the step of determining a plurality of drought-flood transition events according to each of the target months and each of the target drought-flood patches, extracting three-dimensional drought-flood features of each of the drought-flood transition events using the run theory, and outputting a plurality of characteristic attributes of each of the drought-flood transition events includes:

[0025] Performing planar projections on target drought and flood patches of multiple adjacent target months respectively to determine overlapping areas of the target drought and flood patches of each of the adjacent target months;

[0026] Respectively comparing each of the overlapping areas with a preset area threshold;

[0027] Determine the target drought and flood patches in adjacent target months corresponding to any overlapping area greater than the preset area threshold as belonging to the same drought-flood abrupt transition event;

[0028] The run theory is used to extract three-dimensional drought-flood features of each drought-flood sudden transition event, and multiple characteristic attributes of each drought-flood sudden transition event are output.

[0029] Optionally, the multiple characteristic attributes include a duration characteristic attribute, a cumulative impact area characteristic attribute, a cumulative intensity characteristic attribute, and a frequency characteristic attribute; and the step of generating a risk quantification result of each drought-flood transition event according to the multiple characteristic attributes of each drought-flood transition event includes:

[0030] Normalizing the cumulative intensity characteristic attributes and frequency characteristic attributes of each of the drought-flood transition events to determine the normalized cumulative intensity characteristic attributes and normalized frequency characteristic attributes of each of the drought-flood transition events;

[0031] Based on the preset infinite property weight parameters, the normalized cumulative intensity characteristic attributes, normalized frequency characteristic attributes, duration characteristic attributes, and cumulative impact area characteristic attributes of each drought-flood transition event are used to calculate the risk quantification results of each drought-flood transition event.

[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; β 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 quantifying the risk of a drought-flood transition event process, comprising:

[0036] A response module, used to respond to the quantification request, grid the research basin, determine the gridded basin, and obtain the precipitation evapotranspiration data of multiple grid points in the gridded 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] An extraction module, for extracting drought and waterlogging patches from each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and determining a plurality of target months and a plurality of target drought and waterlogging patches;

[0039] An output module is used to determine multiple drought-flood transition events according to each of the target months and each of the target drought-flood patches, and use the run theory to perform three-dimensional drought-flood feature extraction on each of the drought-flood transition events, and output multiple characteristic attributes of each of the drought-flood transition events;

[0040] A generation module is used to generate risk quantification results of each drought-flood sudden transition event according to multiple characteristic attributes of each drought-flood sudden transition event.

[0041] 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 quantifying the risk of drought-flood sudden transition events as described in any one of the above items.

[0042] 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 quantifying the risk of drought-flood sudden transition events as described in any one of the above items.

[0043] 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 quantifying the risk of sudden drought-flood transition events as described in any one of the above items.

[0044] It can be seen from the above technical solutions that the present invention has the following advantages:

[0045] The above technical scheme of the present invention provides a method for quantifying the risk of drought-flood sudden transition event process. When it is necessary to quantify the risk of drought-flood sudden transition event process in the research basin, the research basin is firstly gridded to determine the gridded basin, and the precipitation evapotranspiration data of multiple grid points in the gridded 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; based on the standardized precipitation evapotranspiration index of each grid point in each month, drought-flood patches are extracted for each grid point to determine multiple target months and multiple target drought-flood patches; according to each target month and each target drought-flood patch, multiple drought-flood sudden transition events are determined. The journey theory is used to extract three-dimensional drought-flood features of each drought-flood sudden transition event, and multiple characteristic attributes of each drought-flood sudden transition event are output; finally, the risk quantification results of each drought-flood sudden transition event are generated according to the multiple characteristic attributes of each drought-flood sudden transition event; Based on the above scheme, the journey theory is used to extract three-dimensional drought-flood features of each determined drought-flood sudden transition event, and multiple characteristic attributes of each drought-flood sudden transition event are output, and the risk quantification results of each drought-flood sudden transition event are generated according to the multiple characteristic attributes of each drought-flood sudden transition event. This process comprehensively considers multiple characteristics of drought-flood sudden transition events, quantifies the comprehensive risk of drought-flood sudden transition disasters in the study basin, and can comprehensively reflect the overall risk situation of drought-flood sudden transition disasters to water resources security in the study basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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.

[0047] Figure 1A flowchart of the steps of a method for quantifying the risk of a drought-flood sudden change event process provided in the first embodiment of the present invention;

[0048] Figure 2 A quantitative comprehensive risk distribution map of drought-flood sudden change events provided in the first embodiment of the present invention;

[0049] Figure 3 A flowchart of the steps of a method for quantifying the risk of a drought-flood sudden change event process provided in the second embodiment of the present invention;

[0050] Figure 4 A schematic diagram of a process flow of a method for quantifying the risk of a drought-flood transition event process provided in the second embodiment of the present invention;

[0051] Figure 5 This is a structural block diagram of a device for quantifying the risk of drought-flood sudden transition events provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0052] The embodiments of the present invention provide a method and device for quantifying the risk of drought-flood transition events, which are used to solve the technical problem that the existing analysis methods of drought-flood transition events cannot comprehensively reflect the overall risk situation of drought-flood transition disasters to water resources security in the basin.

[0053] 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.

[0054] See also Figure 1 , Figure 1 This is a flowchart of the steps of a method for quantifying the risk of drought-flood sudden transition events provided in Example 1 of the present invention.

[0055] The present invention provides a method for quantifying the risk of drought-flood sudden change events, comprising:

[0056] Step 101, in response to a quantization 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.

[0057] 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.

[0058] In this embodiment, in response to a quantization 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.

[0059] 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.

[0060] Precipitation evapotranspiration data include monthly precipitation and meteorological temperature data.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] Step 103: extract drought and flood patches from each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and determine multiple target months and multiple target drought and flood patches.

[0065] The target drought and flood patches include target drought patches and target flood patches.

[0066] 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. 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 in 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, then the table It indicates that there is no disaster at the grid point corresponding to the standardized precipitation evapotranspiration index in that month. If a certain standardized precipitation evapotranspiration index in a certain month is not within the preset index range, it indicates that there is a disaster at the grid point corresponding to the standardized precipitation evapotranspiration index in that month, and the grid point corresponding to the standardized precipitation evapotranspiration index is used as the intermediate grid point of that month. For example, in February, there are two standardized precipitation evapotranspiration indices that are not within the preset index range, then the grid points corresponding to these two standardized precipitation evapotranspiration indices are used as the intermediate grid points of February. Based on the above steps and principles, multiple or one intermediate grid points corresponding to multiple months can be obtained.

[0067] Based on the above foundation, according to the disasters existing in each intermediate grid point, that is, the drought or flood state, the spatial clustering method is used to perform spatiotemporal clustering on each intermediate grid point according to the month, and multiple intermediate drought and flood patches are determined. The intermediate drought and flood patches include intermediate drought patches and intermediate flood patches. For example, assuming that according to the above steps, there are ten intermediate grid points in February and twenty intermediate grid points in March, then according to the drought state or flood state corresponding to each intermediate grid point in February, the spatial clustering method is used to perform spatiotemporal clustering on the ten intermediate grid points in February, and multiple or one intermediate drought patch and multiple or one intermediate flood patch in February are obtained. According to the drought state or flood state corresponding to each intermediate grid point in March, the spatial clustering method is used to perform spatiotemporal clustering on the ten intermediate grid points in February, and multiple or one intermediate drought patch and multiple or one intermediate flood patch are obtained. The spatial clustering method is used to perform spatiotemporal clustering on the twenty intermediate grid points in March to obtain multiple or one intermediate drought patches and multiple or one intermediate flood patches in March; wherein, the drought or flood status includes drought status (drought disaster) and flood status (waterlogging disaster), and the preset index range value is [-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 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 in the corresponding month.

[0068] Further, the area of ​​each intermediate drought and waterlogging patch is compared with the preset area threshold; any intermediate drought and waterlogging patch larger than the preset area threshold is taken as the target drought and waterlogging patch, and the month in which the target drought and waterlogging patch exists is taken as the target month; for example, if the above steps show that there is an intermediate drought and waterlogging patch in February, the area of ​​the intermediate drought and waterlogging patch is compared with the preset area threshold; if the area of ​​the intermediate drought and waterlogging patch is less than or equal to the preset area threshold, the intermediate drought and waterlogging patch is removed; if the area of ​​the intermediate drought and waterlogging patch is larger than the preset area threshold, the intermediate drought and waterlogging patch is taken as the target drought and waterlogging patch, and the month in which the target drought and waterlogging patch exists is taken as the target month. February with marked drought and waterlogging patches is taken as the target month; if there are two intermediate drought and waterlogging patches in February according to the above steps, the areas of the two intermediate drought and waterlogging patches are compared with the preset area thresholds respectively; if the areas of the two intermediate drought and waterlogging patches are both less than or equal to the preset area thresholds, the two intermediate drought and waterlogging patches are eliminated; if the areas of the two intermediate drought and waterlogging patches are both greater than the preset area thresholds, the two intermediate drought and waterlogging patches are taken as the corresponding target drought and waterlogging patches, and February with the target drought and waterlogging patches is taken as the target month; wherein, the preset area threshold can be set as needed, and the present invention is not limited to this.

[0069] For example, given an area threshold A th (preset area threshold), based on the spatial clustering method of the R language package "dbscan", for each month (t=1,2,...,n t ) is larger than the area of ​​the middle drought patch in A th The intermediate drought patches are identified to obtain multiple target drought patches, wherein the intermediate drought patches are numbered with different numbers (l = 1, 2, ..., n l ), and obtain the intermediate drought patch number matrix L; this matrix has the same size as the three-dimensional drought indicator matrix (i.e., size 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 positions identified as drought patches (i.e., the grid points are in drought state in the corresponding month) are marked with drought number l, and the unidentified positions (i.e., the grid points do not have disasters in the corresponding month) are marked with 0. For a given drought indicator critical value X0 (valued at -1), the intermediate drought patch number matrix can be expressed as:

[0070]

[0071] Among them, L(lon,lat,t) is the intermediate drought patch number matrix; l is the drought number; X(lon,lat,t) is the standardized precipitation evapotranspiration index of the grid point; X0 is the critical value of the drought index.

[0072] It is worth mentioning that the principle of the steps for determining the target flood patch is consistent with the principle of the steps for determining the target drought patch mentioned above, and the present invention will not elaborate on it any more.

[0073] In this embodiment, drought and flood patches are extracted for each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and a plurality of target months and a plurality of target drought and flood patches are determined.

[0074] Step 104: determine multiple drought-flood transition events according to each target month and each target drought-flood patch, and use the run theory to extract three-dimensional drought-flood features of each drought-flood transition event, and output multiple characteristic attributes of each drought-flood transition event.

[0075] Specifically, planar projections are performed on target drought and flood patches of multiple adjacent target months to determine the overlapping areas of the target drought and flood patches of each adjacent target month; each overlapping area is compared with a preset area threshold; any target drought and flood patches of adjacent target months corresponding to an overlapping area greater than the preset area threshold are determined to belong to the same drought-flood sudden transition event; the run theory is used to extract three-dimensional drought and flood features of each drought-flood sudden transition event, and multiple characteristic attributes of each drought-flood sudden transition event are output.

[0076] For example, suppose that there are two corresponding target drought and flood patches A in two adjacent months t and t+1 respectively. t and A t+1 , the projections of the two target drought and flood patches on the plane have an overlapping area A overlap . When A overlap Greater than the area critical value A th (preset area threshold), it is considered that A t and A t+1 The same drought-flood transition event, on the contrary, overlap If it is less than or equal to the critical area value, then A t and A t+1 Do not belong to the same drought-flood abrupt change event. Similarly, compare any pair of target drought-flood patches in the entire study area at two adjacent moments t and t+1, that is, for each adjacent month t and t+1, the two target drought-flood patches A t and A t+1The corresponding overlapping area is determined by plane projection and compared with the preset area threshold, and the target drought and flood patches belonging to the same drought-flood transition event are renumbered. Finally, for the entire drought and flood index numbering matrix, the positions belonging to the same drought-flood transition event are assigned the same and unique number; among them, the overlapping area A overlap Take an area size of 1.6% of the study area.

[0077] It is worth mentioning that, assuming there are two target drought-flood patches in January and one target drought-flood patch in February, if the overlapping areas of the two target drought-flood patches in January and one target drought-flood patch in February are both greater than the preset area threshold, the two target drought-flood patches in January and the one target drought-flood patch in February are determined to belong to the same drought-flood sudden transition event.

[0078] In this embodiment, multiple drought-flood transition events are determined according to each target month and each target drought-flood patch, and the three-dimensional drought-flood feature extraction of each drought-flood transition event is performed using the run theory to output multiple characteristic attributes of each drought-flood transition event.

[0079] Step 105: Generate risk quantification results of each drought-flood transition event based on multiple characteristic attributes of each drought-flood transition event.

[0080] It should be noted that the multiple characteristic attributes include duration characteristic attribute, cumulative impact area characteristic attribute, cumulative intensity characteristic attribute and frequency characteristic attribute; among which, the duration characteristic attribute is the time interval between the first target drought-flood patch and the last target drought-flood patch of a drought-flood sudden transition event; the cumulative impact area characteristic attribute is that there is an impact area of ​​drought-flood for each month of the drought-flood sudden transition event, and the area obtained by spatially projecting and superimposing the drought-flood impact areas of all months is the cumulative impact area of ​​drought-flood; the cumulative intensity characteristic attribute is the degree of damage of a drought-flood sudden transition event. For each month of the drought-flood sudden transition event, the drought-flood intensity of that month can be calculated (S′1, S'2,..., S' m ), and then the drought and flood intensities of all months are accumulated to obtain the cumulative intensity of drought and flood (cumulative intensity characteristic attribute). The frequency characteristic attribute is the total number of drought and flood abrupt transition events extracted from the target basin with frequency F (frequency, dimensionless). The extraction process of the cumulative intensity characteristic attribute can be expressed as:

[0081]

[0082] Among them, S is the cumulative intensity characteristic attribute; S' m is the drought or flood severity in the mth month; n lon is the number of grids in the longitude direction; n latis the number of grids in the latitude direction; i is the grid data sequence in the longitude direction (i=1,2,3,…,n lon ); j is the grid data sequence in the latitude direction (j=1,2,3,…,n lat ); X(lon,lat,m) is the standardized precipitation evapotranspiration index of the grid point; Area (lon,lat,m) is the area of ​​the grid point; S1' is the drought or flood intensity in the first month; S'2 is the drought or flood intensity in the second month.

[0083] Specifically, see Figure 2 , normalize the cumulative intensity characteristic attributes and frequency characteristic attributes of each drought-flood transition event, and determine the normalized cumulative intensity characteristic attributes and normalized frequency characteristic attributes of each drought-flood transition event; then based on the comprehensive weighted method, quantify the risk process of drought-flood transition events, that is, based on the preset unquantifiable weight parameters, use the normalized cumulative intensity characteristic attributes, normalized frequency characteristic attributes, duration characteristic attributes, and cumulative impact area characteristic attributes of each drought-flood transition event to calculate the risk quantification results of each drought-flood transition event; among which, the preset unquantifiable weight parameters include the first unquantifiable weight parameter and the second unquantifiable weight parameter, and the processing process of the risk quantification results can be expressed as:

[0084]

[0085] Among them, I is the risk quantification result; A is the characteristic attribute of the cumulative impact area; D is the characteristic attribute of the duration; is the normalized cumulative intensity characteristic attribute; is the normalized frequency feature attribute; a is the first infinite property weight parameter, and its value is 0.5; b is the second infinite property weight parameter, and its value is 0.5.

[0086] In this embodiment, risk quantification results of each drought-flood transition event are generated according to multiple characteristic attributes of each drought-flood transition event.

[0087] In an embodiment of the present invention, the present invention provides a method for quantifying the risk of drought-flood sudden transition event process. When it is necessary to quantify the risk of drought-flood sudden transition event process in a research basin, the research basin is firstly gridded to determine the gridded basin, and the precipitation evapotranspiration data of multiple grid points in the gridded 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; based on the standardized precipitation evapotranspiration index of each grid point in each month, drought-flood patches are extracted for each grid point to determine multiple target months and multiple target drought-flood patches; according to each target month and each target drought-flood patch, multiple drought-flood sudden transition events are determined. , and use the tour theory to extract three-dimensional drought and flood features of each drought-flood sudden transition event, and output multiple characteristic attributes of each drought-flood sudden transition event; finally, according to the multiple characteristic attributes of each drought-flood sudden transition event, the risk quantification results of each drought-flood sudden transition event are generated; Based on the above scheme, the tour theory is used to extract three-dimensional drought and flood features of each determined drought-flood sudden transition event, output multiple characteristic attributes of each drought-flood sudden transition event, and according to the multiple characteristic attributes of each drought-flood sudden transition event, the process of generating risk quantification results of each drought-flood sudden transition event comprehensively considers multiple characteristics of drought-flood sudden transition events, quantifies the comprehensive risk of drought-flood sudden transition disasters in the study basin, and can comprehensively reflect the overall risk situation of drought-flood sudden transition disasters to water resources security in the study basin.

[0088] See also Figure 3 , Figure 3 This is a flowchart of the steps of a method for quantifying the risk of drought-flood sudden transition events provided in Example 2 of the present invention.

[0089] The present invention provides a method for quantifying the risk of a flood-induced sudden change event process, comprising:

[0090] Step 301: In response to a quantization 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.

[0091] It should be noted that the precipitation evapotranspiration data can be global hydrological data and meteorological data from 1982 to 2015. The meteorological data used in this embodiment can be derived from the fifth-generation reanalysis global climate data set of the European Center for Medium-Range Weather Forecasts, the groundwater reserve change is derived from the land water reserve change data set of the Gravity Recovery and Climate Experiment GRACE launched by NASA, and the actual evapotranspiration data is derived from the global land evapotranspiration data set based on the PEW model of the National Tibetan Plateau Science Data Center.

[0092] In this embodiment, in response to a quantization 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.

[0093] 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.

[0094] Precipitation evapotranspiration data include monthly precipitation and meteorological temperature data; among them, meteorological temperature data include surface net radiation, wind speed at 2 meters high, atmospheric temperature, daily maximum temperature, daily minimum temperature, and dew point temperature.

[0095] Further, step 302 may include the following sub-steps:

[0096] 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;

[0097] 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 meteorological temperature data of each grid point in each month is substituted into the preset formula group to calculate the potential evapotranspiration of each grid point in each month. The preset formula group can be expressed as:

[0098]

[0099] 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.

[0100] 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;

[0101] 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:

[0102] D i =P i -PET i ;

[0103] Among them, D i is the climate water balance of the ith month (original data series); P i is the climate water balance in the ith month; PET i is the potential evapotranspiration (potential evapotranspiration data) of the i-th month.

[0104] It is worth mentioning that for the climate water balance at other time scales, it can be expressed as:

[0105]

[0106] 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.

[0107] 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;

[0108] 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;

[0109] 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.

[0110] 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.

[0111] 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:

[0112]

[0113] 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 i The 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; β 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.

[0114] Furthermore, the exponential operation function is preset, specifically:

[0115]

[0116] 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.

[0117] 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.

[0118] Step 303: extract drought and flood patches from each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and determine multiple target months and multiple target drought and flood patches.

[0119] Further, step 303 may include the following sub-steps:

[0120] S31, respectively judging whether each standardized precipitation evapotranspiration index is within a preset index range;

[0121] S32, according to each month, taking any grid point corresponding to the standardized precipitation evapotranspiration index that is not within the preset index range as the middle grid point of each month;

[0122] S33, based on the drought and flood status of each intermediate grid point, using the spatial clustering method to perform spatiotemporal clustering on each intermediate grid point according to the month, and determining multiple intermediate drought and flood patches;

[0123] S34, comparing the area of ​​each intermediate drought and flood patch with a preset area threshold;

[0124] S35. Any intermediate drought-waterlogging patch that is larger than a preset area threshold is taken as a target drought-waterlogging patch, and the month in which the target drought-waterlogging patch exists is taken as a target month.

[0125] In this embodiment, drought and flood patches are extracted for each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and a plurality of target months and a plurality of target drought and flood patches are determined.

[0126] Step 304: determine multiple drought-flood transition events according to each target month and each target drought-flood patch, and use the run theory to extract three-dimensional drought-flood features of each drought-flood transition event, and output multiple characteristic attributes of each drought-flood transition event.

[0127] Further, step 304 may include the following sub-steps:

[0128] S41, performing planar projection on target drought and flood patches of multiple adjacent target months respectively, and determining the overlapping areas of target drought and flood patches of each adjacent target month;

[0129] S42, comparing each overlapping area with a preset area threshold;

[0130] S43, determining the target drought and flood patches in adjacent target months corresponding to any overlapping area greater than a preset area threshold as belonging to the same drought-flood transition event;

[0131] S44. Use the run theory to extract three-dimensional drought-flood features of each drought-flood sudden transition event, and output multiple characteristic attributes of each drought-flood sudden transition event.

[0132] It should be noted that the duration, frequency, intensity and affected area characteristics of drought-flood abrupt transition events are extracted based on the run theory, that is, through three-dimensional drought-flood recognition, spatiotemporal continuous drought events can be obtained, and then the characteristic attributes of drought events can be extracted. Multiple characteristic attributes include duration characteristic attributes, cumulative affected area characteristic attributes, cumulative intensity characteristic attributes and frequency characteristic attributes.

[0133] In this embodiment, multiple drought-flood transition events are determined according to each target month and each target drought-flood patch, and the three-dimensional drought-flood feature extraction of each drought-flood transition event is performed using the run theory to output multiple characteristic attributes of each drought-flood transition event.

[0134] Step 305: normalize the cumulative intensity characteristic attributes and frequency characteristic attributes of each drought-flood transition event to determine the normalized cumulative intensity characteristic attributes and normalized frequency characteristic attributes of each drought-flood transition event.

[0135] In this embodiment, the cumulative intensity characteristic attributes and frequency characteristic attributes of each drought-flood transition event are normalized to determine the normalized cumulative intensity characteristic attributes and normalized frequency characteristic attributes of each drought-flood transition event.

[0136] Step 306: Based on the preset infinite property weight parameters, the normalized cumulative intensity characteristic attribute, normalized frequency characteristic attribute, duration characteristic attribute, and cumulative impact area characteristic attribute of each drought-flood transition event are used to calculate the risk quantification results of each drought-flood transition event.

[0137] In this embodiment, based on the preset infinite property weight parameters, the normalized cumulative intensity characteristic attributes, normalized frequency characteristic attributes, duration characteristic attributes, and cumulative impact area characteristic attributes of each drought-flood transition event are used to calculate the risk quantification results of each drought-flood transition event.

[0138] As a comparison of technical effects, it can be combined with existing technologies for reference. Existing studies often only consider the characteristics of drought-flood transition events separately, lack analysis of the comprehensive impact, and cannot comprehensively reflect the overall risk of drought-flood transition disasters to water resources security in the basin. Therefore, comprehensively considering the different characteristics of drought-flood transition events and quantifying the comprehensive risk of drought-flood transition disasters in the basin can provide a scientific basis for the prevention and forecast of drought-flood transition disasters, which is of great significance for the research and early warning of complex disasters in the basin.

[0139] For the above questions, please refer to Figure 4The present invention provides a method for quantifying the risk of drought-flood abrupt transition events. First, the meteorological data of the watershed (precipitation evapotranspiration data), i.e., meteorological data such as precipitation, wind speed, and radiation, are collected. That is, the study area is determined, and the monthly precipitation and potential evapotranspiration data in the watershed are collected. Based on the precipitation data of the watershed, the standardized precipitation evapotranspiration SPEI index is calculated. The spatial clustering method is used to extract drought patches and flood patches from the grid; the spatial clustering method is used to extract drought and flood patches from the grid; the drought and flood patches with drought-flood abrupt transition characteristics are extracted through temporal clustering analysis; the duration, frequency, intensity, and affected area characteristics of drought-flood abrupt transition events are extracted based on the run theory; and finally, the risk of drought-flood abrupt transition events is quantified based on the comprehensive weighted method. The method of the present invention can effectively evaluate the defense line of the evolution of the drought-flood abrupt transition process in the watershed, and provide a scientific basis for the comprehensive prevention and control of complex disasters in the watershed in the future.

[0140] In an embodiment of the present invention, the present invention provides a method for quantifying the risk of drought-flood sudden transition event process. When it is necessary to quantify the risk of drought-flood sudden transition event process in a research basin, the research basin is firstly gridded to determine the gridded basin, and the precipitation evapotranspiration data of multiple grid points in the gridded 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; based on the standardized precipitation evapotranspiration index of each grid point in each month, drought-flood patches are extracted for each grid point to determine multiple target months and multiple target drought-flood patches; according to each target month and each target drought-flood patch, multiple drought-flood sudden transition events are determined. , and use the tour theory to extract three-dimensional drought and flood features of each drought-flood sudden transition event, and output multiple characteristic attributes of each drought-flood sudden transition event; finally, according to the multiple characteristic attributes of each drought-flood sudden transition event, the risk quantification results of each drought-flood sudden transition event are generated; Based on the above scheme, the tour theory is used to extract three-dimensional drought and flood features of each determined drought-flood sudden transition event, output multiple characteristic attributes of each drought-flood sudden transition event, and according to the multiple characteristic attributes of each drought-flood sudden transition event, the process of generating risk quantification results of each drought-flood sudden transition event comprehensively considers multiple characteristics of drought-flood sudden transition events, quantifies the comprehensive risk of drought-flood sudden transition disasters in the study basin, and can comprehensively reflect the overall risk situation of drought-flood sudden transition disasters to water resources security in the study basin.

[0141] See also Figure 5 , Figure 5 This is a structural block diagram of a device for quantifying the risk of drought-flood sudden transition events provided in Example 3 of the present invention.

[0142] The present invention provides a device for quantifying the risk of drought-flood rapid transition events, comprising:

[0143] The response module 501 is used to respond to the quantification request, grid the research basin, determine the gridded basin, and obtain the precipitation evapotranspiration data of multiple grid points in the gridded basin in each month;

[0144] 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;

[0145] Extraction module 503, for extracting drought and flood patches from each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and determining multiple target months and multiple target drought and flood patches;

[0146] The output module 504 is used to determine multiple drought-flood transition events according to each target month and each target drought-flood patch, and use the run theory to extract three-dimensional drought-flood features of each drought-flood transition event, and output multiple characteristic attributes of each drought-flood transition event;

[0147] The generation module 505 is used to generate risk quantification results of each drought-flood transition event according to multiple characteristic attributes of each drought-flood transition event.

[0148] Further, the precipitation evapotranspiration data includes monthly precipitation and meteorological temperature data; according to module 502, it is specifically used for:

[0149] 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;

[0150] 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;

[0151] 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;

[0152] Normalize the cumulative distribution function of each grid point in each month to determine the exceedance probability of each grid point in each month;

[0153] 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.

[0154] Furthermore, the extraction module 503 is specifically used for:

[0155] Determine whether each standardized precipitation evapotranspiration index is within the preset index range;

[0156] 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;

[0157] Based on the drought and flood status of each intermediate grid point, the spatial clustering method is used to perform spatiotemporal clustering of each intermediate grid point according to the month to determine multiple intermediate drought and flood patches;

[0158] The area of ​​each intermediate drought-waterlogged patch was compared with the preset area threshold;

[0159] Any intermediate drought-waterlogging patch that is larger than the preset area threshold is taken as the target drought-waterlogging patch, and the month in which the target drought-waterlogging patch exists is taken as the target month.

[0160] Furthermore, the output module 504 is specifically configured to:

[0161] Performing plane projections on target drought and flood patches of multiple adjacent target months respectively, and determining the overlapping areas of target drought and flood patches of each adjacent target month;

[0162] Compare each overlapping area with a preset area threshold respectively;

[0163] The target drought and flood patches in adjacent target months corresponding to any overlapping area greater than a preset area threshold are determined to belong to the same drought-flood abrupt transition event;

[0164] The run theory is used to extract three-dimensional drought-flood features of each drought-flood transition event, and multiple characteristic attributes of each drought-flood transition event are output.

[0165] Furthermore, the multiple characteristic attributes include a duration characteristic attribute, a cumulative impact area characteristic attribute, a cumulative intensity characteristic attribute, and a frequency characteristic attribute; the generation module 505 is specifically used to:

[0166] Normalizing the cumulative intensity characteristic attributes and frequency characteristic attributes of each drought-flood transition event to determine the normalized cumulative intensity characteristic attributes and normalized frequency characteristic attributes of each drought-flood transition event;

[0167] Based on the preset infinite property weight parameters, the risk quantification results of each drought-flood transition event are calculated using the normalized cumulative intensity characteristic attributes, normalized frequency characteristic attributes, duration characteristic attributes, and cumulative affected area characteristic attributes of each drought-flood transition event.

[0168] Furthermore, the exponential operation function is preset, specifically:

[0169]

[0170] 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; β 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.

[0171] 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.

[0172] 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 quantifying the risk of drought-flood sudden transition events in any of the above embodiments.

[0173] 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 quantifying the risk of drought-flood transition events in any of the above embodiments are implemented.

[0174] 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 quantifying the risk of drought-flood sudden transition events in any of the above embodiments.

[0175] 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.

[0176] 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.

[0177] 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 quantifying the risk of drought-flood sudden change events, characterized in that: include: In response to the quantification 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; Based on the standardized precipitation evapotranspiration index of each grid point in each month, drought and flood patches are extracted for each grid point to determine multiple target months and multiple target drought and flood patches; According to each of the target months and each of the target drought-flood patches, a plurality of drought-flood abrupt transition events are determined, and three-dimensional drought-flood feature extraction is performed on each of the drought-flood abrupt transition events using the run theory, and a plurality of characteristic attributes of each of the drought-flood abrupt transition events are output; Generating risk quantification results of each drought-flood sudden transition event according to multiple characteristic attributes of each drought-flood sudden transition event; The step of extracting drought and flood patches from each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and determining multiple target months and multiple target drought and flood patches 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; Based on the drought and flood status of each of the intermediate grid points, the spatial clustering method is used to perform spatiotemporal clustering on each of the intermediate grid points according to the month to determine a plurality of intermediate drought and flood patches; Respectively comparing the area of ​​each intermediate drought and flood patch with a preset area threshold; Any intermediate drought or flood patch that is larger than the preset area threshold is taken as a target drought or flood patch, and the month in which the target drought or flood patch exists is taken as a target month.

2. The method for quantifying the risk of drought-flood sudden change 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 quantifying the risk of drought-flood sudden change events according to claim 1 is characterized in that: The step of determining a plurality of drought-flood transition events according to each of the target months and each of the target drought-flood patches, extracting three-dimensional drought-flood features of each of the drought-flood transition events using the run theory, and outputting a plurality of characteristic attributes of each of the drought-flood transition events includes: Performing planar projections on target drought and flood patches of multiple adjacent target months respectively to determine overlapping areas of the target drought and flood patches of each of the adjacent target months; Comparing each of the overlapping areas with a preset area threshold respectively; Determine the target drought and flood patches in adjacent target months corresponding to any overlapping area greater than the preset area threshold as belonging to the same drought-flood abrupt transition event; The run theory is used to extract three-dimensional drought-flood features of each drought-flood sudden transition event, and multiple characteristic attributes of each drought-flood sudden transition event are output.

4. The method for quantifying the risk of drought-flood sudden change events according to claim 1 is characterized in that: The multiple characteristic attributes include a duration characteristic attribute, a cumulative impact area characteristic attribute, a cumulative intensity characteristic attribute and a frequency characteristic attribute; The step of generating risk quantification results of each drought-flood transition event according to multiple characteristic attributes of each drought-flood transition event comprises: Normalizing the cumulative intensity characteristic attributes and frequency characteristic attributes of each of the drought-flood transition events to determine the normalized cumulative intensity characteristic attributes and normalized frequency characteristic attributes of each of the drought-flood transition events; Based on the preset infinite property weight parameters, the normalized cumulative intensity characteristic attributes, normalized frequency characteristic attributes, duration characteristic attributes, and cumulative impact area characteristic attributes of each drought-flood transition event are used to calculate the risk quantification results of each drought-flood transition event.

5. The method for quantifying the risk of drought-flood sudden change events according to claim 2 is characterized in that: The preset exponential operation function is specifically: Where 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 three-variable Log-logistic distribution; 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; β 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; γ is the starting parameter.

6. A device for quantifying the risk of drought-flood sudden change events, characterized in that: include: A response module, used to respond to the quantification request, grid the research basin, determine the gridded basin, and obtain the precipitation evapotranspiration data of multiple grid points in the gridded 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; An extraction module, for extracting drought and waterlogging patches from each grid point based on the standardized precipitation evapotranspiration index of each grid point in each month, and determining a plurality of target months and a plurality of target drought and waterlogging patches; An output module is used to determine multiple drought-flood transition events according to each of the target months and each of the target drought-flood patches, and use the run theory to perform three-dimensional drought-flood feature extraction on each of the drought-flood transition events, and output multiple characteristic attributes of each of the drought-flood transition events; A generation module, used for generating risk quantification results of each drought-flood sudden transition event according to multiple characteristic attributes of each drought-flood sudden transition event; The extraction module is specifically used for: 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; Based on the drought and flood status of each of the intermediate grid points, the spatial clustering method is used to perform spatiotemporal clustering on each of the intermediate grid points according to the month to determine a plurality of intermediate drought and flood patches; Respectively comparing the area of ​​each intermediate drought and flood patch with a preset area threshold; Any intermediate drought or flood patch that is larger than the preset area threshold is taken as a target drought or flood patch, and the month in which the target drought or flood patch exists is taken as a target month.

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 quantifying the risk of drought-flood sudden transition event process 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 quantifying the risk of drought-flood transition events as described in any one of claims 1 to 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 quantifying the risk of drought-flood sudden transition events as described in any one of claims 1-5.

Images