A composite dry heat event identification analysis method and system

Abstract

The application belongs to the technical field of extreme climate identification, and discloses a composite dry-hot event identification analysis method and system, wherein the method comprises the following steps: obtaining climate data sequences of a target area, calculating a standardized precipitation evapotranspiration index SPEI sequence and a standardized temperature index STI sequence based on the obtained data; constructing a joint probability distribution function by using the SPEI sequence and the STI sequence, and obtaining a composite dry-hot joint probability model by fitting a Copula function; obtaining a composite dry-hot index based on the composite dry-hot joint probability model, and identifying a composite dry-hot event according to a pre-set index classification threshold. On the basis of the SPEI and the STI, the Copula function is used to construct a joint probability distribution model, the distributions of the two types of meteorological elements are mapped into a unified joint space, the composite risk intensity of drought and high temperature is quantified, the composite dry-hot index is formed, and the probability coupling representation of drought and high temperature is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of extreme climate identification, and more specifically, relates to a method and system for identifying and analyzing complex dry and hot events. Background Technology

[0002] Drought and heat waves are two of the most typical extreme climate events. Their superposition often forms compound drought and heat events (CDHEs), leading to reduced agricultural output, water shortages, ecological degradation, and even public health risks. With global warming and intensified regional human activities, compound drought and heat events are showing a trend of increasing frequency, intensity, and duration in many regions, becoming a key factor affecting watershed water security and sustainable socio-economic development in the region.

[0003] Existing methods for identifying and attributing combined drought and heat events have the following shortcomings: most studies identify events based on SPEI or STI alone, which cannot fully characterize the "drought-high temperature" coupling effect; that is, current research on drought and heat events mainly focuses on a single indicator, which cannot effectively quantify the probability and dependence of the joint occurrence of drought and high temperature in the same spatiotemporal range, and has a large limitation in application. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method and system for identifying and analyzing complex dry and hot events. This system addresses the problem that current research on dry and hot events primarily relies on single indicators to identify events individually, which fails to effectively quantify the probability and dependence of the joint occurrence of drought and high temperatures within the same spatiotemporal range, resulting in significant application limitations. The invention aims to organically combine multi-source meteorological data and joint probability models to achieve accurate identification and analysis of complex dry and hot events.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for identifying and analyzing complex dry heat events is provided, comprising: Acquire climate data sequences for the target region, and calculate standardized precipitation evapotranspiration index and standardized temperature index sequences based on the acquired data; A joint probability distribution function was constructed using the standardized precipitation evapotranspiration index series and the standardized temperature index series, and a combined probability model of dry heat was obtained by fitting the Copula function. The composite dry heat index is obtained based on the standardized precipitation evapotranspiration index, the standardized temperature index, and the combined probability model of dry heat and dryness. The composite dry heat and dryness events are then identified according to the pre-set index classification threshold.

[0006] According to the composite dry-heat event identification and analysis method provided by the present invention, the calculation of the standardized precipitation evapotranspiration index sequence specifically includes: The potential evapotranspiration sequence was calculated using the Thornthwaite method based on the climate data series, and the water balance sequence for each time period was calculated based on the difference between precipitation and potential evapotranspiration. The cumulative probability distribution function is derived by fitting the water balance series using the Log-logistic probability density function, and then the cumulative probability distribution function is standardized to transform the water balance series into a standardized precipitation evapotranspiration index series.

[0007] The method for identifying and analyzing complex dry heat events provided by this invention, k The water balance volume for a period of one month is calculated using the following formula: ; in, In the first i Year j Starting from the month k The cumulative difference between monthly precipitation and potential evapotranspiration; i For the year; j、m For months; k For cumulative months; In the first i Year m The difference between monthly precipitation and potential evapotranspiration.

[0008] According to the method for identifying and analyzing complex hot and dry events provided by this invention, calculating the standardized temperature index sequence specifically includes: based on the temperature sequence in the climate data sequence, using the cumulative distribution function... G ( T Calculate the standardized temperature index STI as follows: ; ; in, T It is a temperature time series. μ and σ These are the mean and standard deviation parameters, respectively. φ It follows a standard normal distribution.

[0009] According to the method for identifying and analyzing complex dry heat events provided by this invention, the acquisition of the complex dry heat index specifically includes: Marginal distribution fitting: Marginal distribution fitting is performed on the standardized precipitation evapotranspiration index series and the standardized temperature index series, respectively. The marginal distribution is one of the normal distribution, Gamma distribution or log-normal distribution. Copula function selection: based on Spearman's ρCorrelation coefficient; select the optimal Copula function, which includes Gumbel Copula, Clayton Copula, or Frank Copula. Joint distribution construction: By fitting the joint probability distribution of the standardized precipitation evapotranspiration index sequence and the standardized temperature index sequence with the optimal Copula function, a combined dry-heat probability model is obtained; Composite dry heat index generation: The composite dry heat joint probability model is mapped to a single-value composite dry heat index.

[0010] The method for identifying and analyzing complex dry heat events provided by the present invention further includes: The target area is divided into grids according to spatial latitude and longitude, and the composite dry heat index sequence of each grid area is obtained based on the climate data sequence of each grid area. Furthermore, based on the three dimensions of time, space, and composite dry heat index, a three-dimensional spatiotemporal feature matrix of composite dry heat events in the target area is constructed. Based on the three-dimensional spatiotemporal feature matrix, the composite dry heat events in the target area during the study period are identified and extracted; Feature indicators of combined dry-heat events in the target region during the study period are extracted; based on the feature indicators, the spatiotemporal variation trend of combined dry-heat events is analyzed to obtain the spatiotemporal evolution law of combined dry-heat events.

[0011] According to the method for identifying and analyzing complex dry and hot events provided by the present invention, the method identifies and extracts complex dry and hot events in a target area during the study period based on the three-dimensional spatiotemporal feature matrix, including: Preliminary identification of composite dry heat grids: Based on the composite dry heat index of each grid region, grid regions that meet the preset index threshold requirements are selected to obtain the preliminary identified composite dry heat grids; Composite dry heat grid screening and determination: Determine whether the sum of the areas of the composite dry heat grids initially identified in all grid regions of the target area at any given time meets the preset area threshold requirement. If it does, then the given time is determined as a composite dry heat event, and the initially identified composite dry heat grids corresponding to the given time are determined as the final composite dry heat grids. Extraction of composite dry heat events: Temporally consecutive composite dry heat events are merged into a single composite dry heat event, and the composite dry heat events within the study period are extracted.

[0012] According to the method for identifying and analyzing complex dry and hot events provided by this invention, the characteristic indicators of complex dry and hot events in the target area during the study period include duration, intensity, peak intensity, and multiple indicators at the centroid; wherein: Duration refers to the time span of the complex dry-heat event; The intensity is the sum of the average composite dry heat indexes at all times within the time span of the composite dry heat event; Kuness is the maximum value among the average values ​​of the composite dry heat index at all times within the time span of the composite dry heat event. The centroid represents the specific location of the composite dry-heat event at any given time. It is the ratio of the longitude-weighted sum to the grid weighted sum of the composite dry-heat grid at any given time, as well as the ratio of the latitude-weighted sum to the grid weighted sum.

[0013] According to the method for identifying and analyzing complex dry-heat events provided by this invention, obtaining the spatiotemporal evolution law of complex dry-heat events further includes: This study analyzes the temporal frequency variations, duration variations, spatial expansion range, and movement trends of complex dry-heat events to elucidate their spatiotemporal evolution patterns; among which: The temporal frequency of the combined dry heat event is the number of times the combined dry heat event occurs within a preset period; The duration of the combined dry heat event is the average duration of the combined dry heat event within the preset period; The spatial extension range is the total coverage area of ​​the composite dry-thermal grid at any time within the composite dry-thermal event; The spatial movement trend is the displacement of the overall center of mass of the complex dry-thermal event from the start to the end.

[0014] According to another aspect of the present invention, a complex dry heat event identification and analysis system is provided. The system includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the complex dry heat event identification and analysis method described in any of the above claims.

[0015] Overall, compared with the prior art, the composite dry heat event identification and analysis method and system provided by this invention offer the following advantages: 1. Based on SPEI and STI, this invention utilizes the Copula function to construct a joint probability distribution model, mapping the distribution of two types of meteorological elements to a unified joint space to obtain the joint occurrence probability of drought and high temperature, thereby quantifying the combined risk intensity of drought and high temperature, forming a new composite dry-heat index, and realizing the probabilistic coupling characterization of drought and high temperature; it can more accurately identify and analyze composite dry-heat events, improving applicability; 2. This invention innovatively proposes a three-dimensional spatiotemporal feature matrix structure, which achieves unified identification of complex dry-heat events by tracking the duration of events in the time dimension, identifying the aggregation of dry-heat regions in the spatial dimension, and characterizing the severity of events in the intensity dimension; 3. This invention designs a composite event feature extraction method, which can automatically calculate the event's duration, intensity, peak value, area of ​​influence, centroid trajectory, and propagation direction from a three-dimensional matrix. Based on this, a time series and spatial migration trajectory dataset is constructed, which can identify the frequency changes, intensity evolution, and spatial migration patterns of composite dry and hot events in different climate stages and regions, providing a physical basis for risk assessment and prediction. 4. This method is applicable to climate risk assessment, agricultural and water resource management, ecological environment protection and disaster prevention and mitigation decision support, and has important application value, especially in regions where compound extreme events occur frequently under the background of global warming. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for identifying and analyzing complex dry heat events provided in an embodiment of the present invention.

[0017] Figure 2 This is a diagram illustrating the extraction process of the three-dimensional spatiotemporal feature matrix provided in an embodiment of the present invention.

[0018] Figure 3 This is a graph showing the evolution trend of the complex dry heat event provided in the embodiments of the present invention.

[0019] Figure 4 This is a diagram of the centroid migration process of a complex dry heat event provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0021] Please see Figure 1 This embodiment provides a method for identifying and analyzing complex dry heat events, which includes: Acquire climate data sequences for the target region, and calculate standardized precipitation evapotranspiration index (SPEI) and standardized temperature index (STI) sequences based on the acquired data; A joint probability distribution function was constructed using the standardized precipitation evapotranspiration index series and the standardized temperature index series, and a combined probability model of dry heat was obtained by fitting the Copula function. The composite dry heat index is obtained based on the standardized precipitation evapotranspiration index, the standardized temperature index, and the combined probability model of dry heat and dryness. The composite dry heat and dryness events are then identified according to the pre-set index classification threshold.

[0022] In some embodiments, the climate data series includes precipitation, evapotranspiration, and temperature data series, which can be daily or monthly scale data, without specific limitation; calculating the standardized precipitation-evapotranspiration index series specifically includes: Potential evapotranspiration sequences were calculated using the Thornthwaite method based on climate data series, according to precipitation. P With potential evaporation PET The difference between them is used to calculate the water balance sequence for each time period; that is, the water balance. It also includes interpolating missing values ​​in climate data series and ensuring the consistency of time series; as well as smoothing and standardizing climate data series. The cumulative probability distribution function is derived by fitting the water balance series using the Log-logistic probability density function, and then the cumulative probability distribution function is standardized to transform the water balance series into a standardized precipitation evapotranspiration index series.

[0023] In some embodiments, the SPEI calculation needs to include the following steps: (1) Calculation using the Thornthwaite method PET The calculation formula is as follows: ; In the formula: T mean The average monthly temperature; N This represents the maximum sunshine duration. NDM The number of days in a month; n To and I The relevant coefficients are calculated using the following formulas: ; I The annual calorie index, For the first j The average monthly temperature over a month is calculated as follows: ; (2) The specific calculation of the water balance sequence for each time period is as follows: k Water balance over a period of one month ,Right now k Cumulative difference between precipitation and potential evapotranspiration over a period of one month Specifically, it is calculated using the following formula: ; ; in, P This refers to precipitation. fork The cumulative amount of precipitation over a period of one month; for k The cumulative potential evapotranspiration over a period of one month; In the first i Year j Starting from the month k The cumulative difference between monthly precipitation and potential evapotranspiration; i The year indicates a specific year; j、m "Month" indicates a specific month within a given year. k For cumulative months; In the first i Year m The difference between monthly precipitation and potential evapotranspiration.

[0024] (3) Fitting the data sequence using the Log-logistic distribution ; ; ; ; Cumulative probability distribution function as follows: ; In the formula: These are the parameters obtained by fitting using the method of moments; This represents the first three probability weighted moments of the water balance sequence; for The gamma function; D This represents the water balance quantity, and the data within the water balance quantity sequence. For cumulative probability, when When ≤ 0.5, The probability weights are ,when When the value is greater than 0.5, the cumulative probability is 1- .

[0025] (4) The sequence is transformed into a standardized normal distribution: ; In the formula: c 0 = 2.515517, c 1 = 0.802853, c 2 = 0.010328, d 1 = 1.432788, d 2 = 0.189269 and d 3 = 0.001308.

[0026] In some embodiments, calculating the standardized temperature index sequence specifically includes: using a cumulative distribution function based on the temperature sequence in the climate data sequence. G ( T Calculate the standardized temperature index STI as follows: ; ; in, T It is a temperature time series. μ and σ These are the mean and standard deviation parameters, respectively. φ It follows a standard normal distribution.

[0027] In some embodiments, obtaining the composite dry heat index specifically includes: Marginal distribution fitting: Marginal distribution fitting is performed on the standardized precipitation evapotranspiration index series and the standardized temperature index series, respectively. The marginal distribution is one of the following: normal distribution, Gamma distribution, or log-normal distribution; specifically: ; Among them SPEI ( ) for time The standardized precipitation evapotranspiration index (STI) is used to determine the value of precipitation evapotranspiration index. () represents the standardized temperature index value over time; F S , F I These are the marginal cumulative distribution functions (CDF) of SPEI and STI, respectively. U ( ) is from SPEI ( The unified variable obtained through probability integral transformation V ( ) is by STI ( The unified variable obtained through probability integral transformation.

[0028] Copula function selection: based on Spearman's ρ Correlation coefficient; select the optimal Copula function, which includes Gumbel Copula, Clayton Copula, or Frank Copula. Joint distribution construction: By fitting the joint probability distribution of the standardized precipitation evapotranspiration index sequence and the standardized temperature index sequence with the optimal Copula function, a combined dry-heat probability model is obtained; The Combined Dry Heat Index (SCDHI) is generated by mapping the combined dry heat probabilistic model to a single-valued combined dry heat index. The threshold range [-∞, -0.5] is used to judge the severity of combined dry heat events. Specifically, [-0.8, -0.5] represents a normal combined dry heat event, [-1.3, -0.8] a mild combined dry heat event, [-1.6, -1.3] a moderate combined event, [-2.0, -1.6] a severe combined event, and [-∞, -2] an extremely severe combined event.

[0029] Specifically, the basic calculation formula for each Copula function of the Gumbel Copula, Clayton Copula, or Frank Copula mentioned above is as follows: Gumbel Copula: ; Clayton Copula: ; Frank Copula: ; Gaussian Copula: ; in, , These are the two marginal variables obtained above. and , These are Copula's dependency parameters, controlling... , The dependence strength and tail-related features of different Copulas The range of values ​​is different. It is the linear correlation parameter in Gaussian Copula, corresponding to the correlation coefficient of a latent two-dimensional normal variable, which usually satisfies ∈( 1,1).

[0030] In some embodiments, the following shortcomings of existing joint probability models are also considered: some methods model the correlation between drought and high temperature through bivariate probability distributions, but often only perform statistical joint probability estimations, lacking event identification and tracking in the spatiotemporal dimension; existing studies mostly focus on frequency or intensity assessment at the regional scale, lacking a systematic approach to reveal the three-dimensional spatiotemporal evolution of compound dry-heat events, such as event duration, spatial expansion, peak characteristics, and centroid movement; furthermore, existing analyses often only provide trends in event frequency and intensity, making it difficult to reveal the propagation path and evolution mechanism of events in the spatiotemporal dimension, limiting their application value in disaster risk management. This leads to the following shortcomings in existing technologies: existing event identification is mostly limited to two-dimensional analysis, ignoring the overall characteristics of events in terms of spatiotemporal continuity and intensity evolution, resulting in unclear event boundaries and difficulty in determining spatial aggregation; existing studies usually focus on the frequency or intensity changes of dry-heat events, lacking systematic extraction and quantification methods for key event characteristics, especially lacking means to dynamically characterize the event evolution process from a three-dimensional spatiotemporal perspective. In summary, there is an urgent need for a method that can organically combine multi-source meteorological data, joint probability models, and three-dimensional spatiotemporal feature matrices to achieve accurate identification and evolutionary pattern analysis of complex dry-heat events.

[0031] Based on this, refer to Figure 2 This embodiment proposes a method and system for identifying and analyzing the evolution of complex dry-heat events based on a three-dimensional spatiotemporal feature matrix, enabling accurate identification and evolutionary pattern analysis of complex dry-heat events. Specifically, the method for identifying and analyzing complex dry-heat events also includes: The target area is divided into grids according to spatial latitude and longitude, and the composite dry heat index sequence of each grid area is obtained based on the climate data sequence of each grid area. Furthermore, based on the three dimensions of time, space, and composite dry heat index, a three-dimensional spatiotemporal feature matrix of composite dry heat events in the target area is constructed. Based on the three-dimensional spatiotemporal feature matrix, the composite dry heat events in the target area during the study period are identified and extracted; Feature indicators of combined dry-heat events in the target region during the study period are extracted; based on the feature indicators, the spatiotemporal variation trend of combined dry-heat events is analyzed to obtain the spatiotemporal evolution law of combined dry-heat events.

[0032] Based on the three dimensions of time, space, and intensity, a three-dimensional spatiotemporal feature matrix of the complex dry heat event is constructed, including: constructing a three-dimensional three-dimensional data space for the complex dry heat index (three-dimensional spatiotemporal feature matrix); calculating the SCDHI sequence; and based on this, constructing a matrix based on longitude (…). Lon (x) is the horizontal axis, and latitude (y) is the vertical axis. Lat ) is the vertical axis, and time ( t A three-dimensional composite dry heat index data matrix with the vertical axis as the y-axis.M ( Lon , Lat , t The dimension of this matrix is: ; In the formula: N Lon To determine the number of grid cells within the longitude range of the study area, N Lat To study the number of grids within the latitudinal range of the region, N t This represents the number of grid cells in the study area during the study period.

[0033] In some embodiments, the identification and extraction of compound dry-heat events in the target region during the study period based on the three-dimensional spatiotemporal feature matrix includes: Preliminary identification of composite dry heat grids: Based on the composite dry heat index of each grid region, grid regions that meet the preset index threshold requirements are selected to obtain preliminary identification of composite dry heat grids; for example, the transition value of the composite dry heat index from mild to moderate -0.8 is selected as the threshold and identification standard for judging whether a certain grid has composite dry heat. If it is less than -0.8, it is judged as a preliminary identification of composite dry heat grids. Further preliminary identification of all composite dry heat grid points in the current time period is carried out, and adjacent composite dry heat grids are marked.

[0034] Composite dry heat grid screening and determination: At any given time, it is determined whether the sum of the areas of the initially identified composite dry heat grids in all grid regions of the target area meets a preset area threshold requirement. If it does, then that time is determined as a composite dry heat event, and the initially identified composite dry heat grids corresponding to that time are determined as the final composite dry heat grids. That is, a composite dry heat area threshold A0 is set. Only when the total grid area A of the initially identified composite dry heat grids in the target area is greater than the composite dry heat threshold area A0 is it considered a composite dry heat event. Only grids that simultaneously meet the initial identification and screening determination conditions are identified as composite dry heat grids. Composite dry heat identification matrix. DM for:

[0035] In the formula: DM ( Lon , Lat , t )for t Time-based composite dry heat identification matrix, s The number of grids for composite dry heat identification. A ( Lon , Lat , t ) represents the grid area of ​​a specific composite dry-thermal event that was identified.

[0036] Extraction of composite dry heat events: Temporally consecutive composite dry heat events are merged into a single composite dry heat event, and the composite dry heat events within the study period are extracted.

[0037] In some embodiments, the characteristic indicators of the combined dry and hot events in the target region during the study period include multiple parameters such as duration, intensity, peak intensity, and centroid; wherein: Duration refers to the time span of the combined dry-heat event; that is, the time span from the start of the combined dry-heat grid to the end of the current combined dry-heat grid. Specifically: ; In the formula: DD The duration of a certain complex dry heat event; T e This refers to the end time of the combined dry heat treatment; T s This is the start time of the combined dry heat treatment.

[0038] The intensity is the sum of the average composite dry heat indexes at all times within the time span of the composite dry heat event; taking monthly data as an example, the intensity is specifically: ; In the formula: For the first time under the combined dry heat continuous state m Months SCDHI The exponential average, that is, the first... m The average of the composite dry heat index for all grid regions corresponding to the month.

[0039] Kurtosis is the maximum value among the average values ​​of the combined dry heat index at all times within the time span of a combined dry heat event; taking monthly data as an example, the formula is as follows: .

[0040] The centroid represents the specific location of a complex dry-thermal event at any given time. It is the ratio of the longitude-weighted sum to the sum of grid weights for the complex dry-thermal event at that time, and also the ratio of the latitude-weighted sum to the sum of grid weights. This is of great significance for characterizing the temporal migration path and spatial evolution trend of complex dry-thermal events. The specific formula is: ; In the formula: w g For the first g The weighting factor of each grid, Lon g and Lat g They are the first g The longitude and latitude of each grid. s This represents the total number of composite dry thermal grids at any given time.

[0041] In some embodiments, obtaining the spatiotemporal evolution law of the complex dry-heat event further includes: This study analyzes the temporal frequency variations, duration variations, spatial expansion range, and movement trends of complex dry-heat events to elucidate their spatiotemporal evolution patterns; among which: The temporal frequency of combined dry heat events is the number of times combined dry heat events occur within a predefined period; for example, in a study period measured in years. TR Within, the number of occurrences of complex dry heat events was statistically analyzed. Num The average annual frequency of the event was obtained. λ y : .

[0042] The persistence of combined dry and hot events is defined as the average duration of such events within a preset period. Persistence variation analysis calculates the average duration of combined dry and hot events for each year or period as follows: ; in, N y For the year y The number of events within, DR x For the first x The duration of each event.

[0043] The spatial extension range is the total coverage area of ​​the composite dry-thermal grid at any time within the composite dry-thermal event; The spatial movement trend is the displacement of the overall centroid of the complex dry-thermal event from its start to its end. Based on the centroid trajectory {( x c ( t ), y c ( t The overall centroid displacement is calculated as follows: .

[0044] In some embodiments, a complex dry heat event identification and analysis system is also provided. The system includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the complex dry heat event identification and analysis method described above.

[0045] This invention discloses a method and system for analyzing the evolution of complex dry-heat events based on a three-dimensional spatiotemporal feature matrix. The method calculates the SPEI and STI based on precipitation, evapotranspiration, and temperature data, and constructs a complex dry-heat index using a Copula function, setting dry-heat thresholds for different intensity levels. Based on the continuity of the complex dry-heat index (SCDHI) in both time and space, complex dry-heat events are identified, and a three-dimensional spatiotemporal feature matrix is ​​constructed. Typical features of complex dry-heat events are extracted from the three-dimensional matrix, including duration, intensity, peak intensity, and centroid trajectory. The spatiotemporal evolution trend of complex dry-heat events is quantitatively analyzed using these feature indicators, revealing the frequency changes, spatial expansion, and movement paths of the events. This invention enables accurate identification and evolution characterization of complex dry-heat events, ensuring that the results are both statistically significant and physically plausible, providing scientific support for watershed water resource management, climate risk assessment, and disaster prevention and mitigation.

[0046] This case study uses a specific watershed as a research example, dividing it into eight sub-basin regions. Furthermore, the research data in this chapter is divided into two parts: the first part is measured data, and the second part is reanalysis data. Measured data from 311 meteorological stations and 27 hydrological stations from upstream to downstream are included, encompassing nine attributes: runoff, precipitation, temperature, relative humidity, air pressure, water vapor pressure, sunshine duration, wind speed, sunshine percentage, and evaporation. The time scale is diurnal, and the time series is from 1975 to 2018. The table below shows the extracted composite dry-heat events in this watershed from 1975 to 2018, comprising a total of 12 events. A plot is then drawn based on these 12 extracted events. Figure 3 and Figure 4 These represent the evolutionary trends and migration processes of events in the basin. Before 1990, the trend was from downstream to mid-upper reaches; after 1990, the trend shifted from upstream to downstream.

[0047]

[0048] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying and analyzing complex dry heat events, characterized in that, include: Acquire climate data sequences for the target region, and calculate standardized precipitation evapotranspiration index and standardized temperature index sequences based on the acquired data; A joint probability distribution function was constructed using the standardized precipitation evapotranspiration index series and the standardized temperature index series, and a combined probability model of dry heat was obtained by fitting the Copula function. The composite dry heat index is obtained based on the standardized precipitation evapotranspiration index, the standardized temperature index, and the combined probability model of dry heat and dryness. The composite dry heat and dryness events are then identified according to the pre-set index classification threshold.

2. The method for identifying and analyzing complex dry heat events as described in claim 1, characterized in that, The calculation of the standardized precipitation evapotranspiration index series specifically includes: The potential evapotranspiration sequence was calculated using the Thornthwaite method based on the climate data series, and the water balance sequence for each time period was calculated based on the difference between precipitation and potential evapotranspiration. The cumulative probability distribution function is derived by fitting the water balance series using the Log-logistic probability density function, and then the cumulative probability distribution function is standardized to transform the water balance series into a standardized precipitation evapotranspiration index series.

3. The method for identifying and analyzing complex dry heat events as described in claim 2, characterized in that, k The water balance volume for a period of one month is calculated using the following formula: ； in, In the first i Year j Starting from the month k The cumulative difference between monthly precipitation and potential evapotranspiration; i For the year; j、m For months; k For cumulative months; In the first i Year m The difference between monthly precipitation and potential evapotranspiration.

4. The method for identifying and analyzing complex dry heat events as described in claim 1, characterized in that, Calculating the standardized temperature index series specifically involves: based on the temperature series in the climate data series, using the cumulative distribution function... G ( T Calculate the standardized temperature index STI as follows: ； ； in, T It is a temperature time series. μ and σ These are the mean and standard deviation parameters, respectively. φ It follows a standard normal distribution.

5. The method for identifying and analyzing complex dry heat events as described in claim 1, characterized in that, The specific steps to obtain the composite dry heat index include: Marginal distribution fitting: Marginal distribution fitting is performed on the standardized precipitation evapotranspiration index series and the standardized temperature index series, respectively. The marginal distribution is one of the normal distribution, Gamma distribution or log-normal distribution. Copula function selection: based on Spearman's ρ Correlation coefficient; select the optimal Copula function, which includes Gumbel Copula, Clayton Copula, or Frank Copula. Joint distribution construction: By fitting the joint probability distribution of the standardized precipitation evapotranspiration index sequence and the standardized temperature index sequence with the optimal Copula function, a combined dry-heat probability model is obtained; Composite dry heat index generation: The composite dry heat joint probability model is mapped to a single-value composite dry heat index.

6. The method for identifying and analyzing complex dry heat events as described in any one of claims 1-5, characterized in that, Also includes: The target area is divided into grids according to spatial latitude and longitude, and the composite dry heat index sequence of each grid area is obtained based on the climate data sequence of each grid area. Furthermore, based on the three dimensions of time, space, and composite dry heat index, a three-dimensional spatiotemporal feature matrix of composite dry heat events in the target area is constructed. Based on the three-dimensional spatiotemporal feature matrix, the composite dry heat events in the target area during the study period are identified and extracted; Feature indicators of combined dry-heat events in the target region during the study period are extracted; based on the feature indicators, the spatiotemporal variation trend of combined dry-heat events is analyzed to obtain the spatiotemporal evolution law of combined dry-heat events.

7. The method for identifying and analyzing complex dry heat events as described in claim 6, characterized in that, Based on the aforementioned three-dimensional spatiotemporal feature matrix, the composite dry-heat events in the target region during the study period are identified and extracted, including: Preliminary identification of composite dry heat grids: Based on the composite dry heat index of each grid region, grid regions that meet the preset index threshold requirements are selected to obtain the preliminary identified composite dry heat grids; Composite dry heat grid screening and determination: Determine whether the sum of the areas of the composite dry heat grids initially identified in all grid regions of the target area at any given time meets the preset area threshold requirement. If it does, then the given time is determined as a composite dry heat event, and the initially identified composite dry heat grids corresponding to the given time are determined as the final composite dry heat grids. Extraction of composite dry heat events: Temporally consecutive composite dry heat events are merged into a single composite dry heat event, and the composite dry heat events within the study period are extracted.

8. The method for identifying and analyzing complex dry heat events as described in claim 7, characterized in that, The characteristic indicators of the complex dry-thermal events in the target area during the study period include duration, intensity, peak intensity, and multiple parameters at the centroid; among which: Duration refers to the time span of the complex dry-heat event; The intensity is the sum of the average composite dry heat indexes at all times within the time span of the composite dry heat event; Kuness is the maximum value among the average values ​​of the composite dry heat index at all times within the time span of the composite dry heat event. The centroid represents the specific location of the composite dry-heat event at any given time. It is the ratio of the longitude-weighted sum to the grid weighted sum of the composite dry-heat grid at any given time, as well as the ratio of the latitude-weighted sum to the grid weighted sum.

9. The method for identifying and analyzing complex dry heat events as described in claim 7, characterized in that, The spatiotemporal evolution of complex dry-heat events also includes: This study analyzes the temporal frequency variations, duration variations, spatial expansion range, and movement trends of complex dry-heat events to elucidate their spatiotemporal evolution patterns; among which: The temporal frequency of the combined dry heat event is the number of times the combined dry heat event occurs within a preset period; The duration of the combined dry heat event is the average duration of the combined dry heat event within the preset period; The spatial extension range is the total coverage area of ​​the composite dry-thermal grid at any time within the composite dry-thermal event; The spatial movement trend is the displacement of the overall center of mass of the complex dry-thermal event from the start to the end.

10. A system for identifying and analyzing complex dry heat events, characterized in that, The system includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the composite dry heat event identification and analysis method according to any one of claims 1-9.

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