A method and system for quantifying and uniformly identifying a combined stress of a daily scale composite drought-heat wave event
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN UNIV
- Filing Date
- 2026-02-10
- Publication Date
- 2026-06-02
Smart Images
Figure CN122132738A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of meteorological disaster monitoring and time-series data processing technology, specifically to a method and system for quantifying and uniformly identifying the joint stress of daily-scale combined drought-heat wave events. Background Technology
[0002] Against the backdrop of global warming, the frequency, intensity, and duration of extreme weather events are showing a significant upward trend. Droughts and heat waves, in particular, often do not occur in isolation but rather are mutually induced and superimposed through land-atmosphere interactions, forming compound drought and heatwaves (CDHWs). The destructive impact of such compound events on agricultural yields, ecosystem carbon sequestration functions, and human health often far exceeds the linear superposition of single disasters. However, existing technologies for identifying and quantifying compound drought and heatwave events still have the following significant shortcomings in engineering applications:
[0003] In existing technologies, drought events are typically characterized using monthly-scale indicators such as the Standardized Precipitation Index (SPI) or the Standardized Precipitation Evapotranspiration Index (SPEI), while heat wave events are mostly identified on a daily scale based on temperature thresholds. Because these two types of indicators are inconsistent in their temporal scale systems, existing methods struggle to construct a unified daily logic for determining joint stress, and are unable to accurately characterize short-duration but high-intensity explosive composite impact processes. Furthermore, some methods only use simple linear superposition to construct composite indicators, failing to reflect the nonlinear synergistic enhancement effect of drought and heat on the same spatiotemporal scale.
[0004] Existing methods for identifying compound events often employ a "strict concurrency" logic, requiring drought and heat waves to occur simultaneously every single day to constitute a compound event. This rigid constraint ignores the physical continuity and phased evolution of disaster processes. In actual observational data, short-term fluctuations in daily meteorological elements often incorrectly fragment a complete, long-duration disaster process into multiple short events, resulting in a "fragmented" event process. In automated batch identification and statistical analysis, this problem severely affects the stability and reliability of indicators such as event duration, intensity, and frequency. Furthermore, existing methods struggle to identify sequentially continuous compound events where "drought occurs first, followed by heat wave" or "heat wave occurs first, followed by drought," failing to characterize the evolutionary structure of compound disasters.
[0005] When assessing the impact of compound events or conducting attribution analyses, it is often necessary to construct "pure drought events" or "pure heat wave events" unaffected by another type of stress as control samples. However, current techniques typically distinguish between single and compound events based solely on whether they occur concurrently within the same event, neglecting potential lag interferences within the temporal neighborhood before and after the event. For example, a drought event may not have a heat wave within it, but if it occurs immediately before or after a heat wave, such events are physically influenced by the compound background. The lack of systematic temporal isolation and purity screening mechanisms can lead to the inclusion of compound stress signals in single event samples, thus lacking a reliable benchmark for subsequent impact assessments and control analyses. Summary of the Invention
[0006] To overcome the problems of inconsistent time scales of joint stress indicators, rigid event identification logic leading to process fragmentation, difficulty in depicting the temporal evolution structure of compound events, and lack of strict separation and comparison mechanisms for pure single events in the identification of compound drought-heat wave events, this invention provides a method and system for joint stress quantification and unified identification of daily-scale compound drought-heat wave events. By uniformly constructing joint stress intensity indicators at the daily scale and combining a continuous run-length scanning method with noise resistance and fault tolerance, it can achieve fine differentiation between concurrent compound drought-heat wave events, continuous compound drought-heat wave events, and single stress events.
[0007] According to one aspect of the present invention, the present invention provides a method for quantifying and uniformly identifying the joint stress of a daily-scale combined drought-heat wave event, comprising: S1: acquiring daily-scale temperature data and land surface moisture data and performing time alignment and spatial consistency preprocessing to construct a daily-scale gridded time series dataset with uniform spatiotemporal resolution; S2: based on the daily-scale gridded time series dataset, performing standardization processing and extreme stress determination on the daily temperature data and land surface moisture data respectively to calculate a daily-scale single stress intensity index; the single stress intensity index includes a heat stress intensity index and a drought stress intensity index; S3: constructing a daily-scale joint stress intensity index based on the daily-scale heat stress intensity index and drought stress intensity index; wherein, the joint stress intensity index is determined only when... When a single stress exists, it degenerates into the corresponding single stress intensity index; S4: Based on the daily-scale heat stress intensity index and drought stress intensity index, the daily stress state is multi-classified discretely encoded to obtain a multi-classified discrete state time series; S5: On the multi-classified discrete state time series, based on the preset fault-tolerant time window and time continuity criterion, the continuous run scanning method is used to uniformly identify the compound drought-heat wave event; the continuous run scanning method is used to scan the continuous time period containing only a single stress state to realize the identification of the single stress event; S6: Based on the joint stress intensity index and its degenerated single stress intensity index, each identified compound drought-heat wave event and single stress event are integrated and processed to construct a structured event record set.
[0008] Further, step S2 includes: determining the high temperature extreme determination threshold and the drought extreme determination threshold based on the statistical distribution of corresponding meteorological elements and moisture elements within a preset historical baseline period; standardizing and determining the extreme stress of daily temperature data based on the high temperature extreme determination threshold to obtain a daily heat stress intensity index; and standardizing and determining the extreme stress of daily land surface moisture data based on the drought extreme determination threshold to obtain a daily drought stress intensity index.
[0009] Furthermore, the daily temperature data and land surface moisture data are standardized and extreme stress assessments are performed. The corresponding formula is:
[0010] ,
[0011] ,
[0012] in, As an indicator of thermal stress intensity, As an indicator of drought stress intensity, and For any grid point in the current study region at the th Daily temperature data and land surface moisture data, The threshold for determining extreme high temperatures. The threshold for determining drought extremes. , These are the scale parameters for scaling temperature data and land surface moisture data, respectively.
[0013] Further, step S4 includes: determining the daily stress state based on the daily heat stress intensity index and drought stress intensity index; classifying the daily stress state into at least four mutually exclusive discrete state categories based on the determination results of the stress state; the discrete state categories include at least no stress state, drought stress only state, heat stress only state, and combined stress state; and assigning corresponding discrete code identifiers to the discrete state categories to form a multi-class discrete state time series.
[0014] Furthermore, based on a preset fault-tolerance time window and a time continuity criterion, a continuous run scanning method is used to uniformly identify complex drought-heat wave events in the multi-class discrete state time series. This includes: in the multi-class discrete state time series, performing continuous run scanning with the complex stress state as the target state; when the target state appears continuously in time and its continuous duration is not less than a preset minimum duration threshold, the corresponding continuous time period is determined as a concurrent complex drought-heat wave event; in the multi-class discrete state time series, drought-only stress state, heat-only stress state, and complex stress state are defined as active states, and no-stress state is defined as inactive state; continuous run scanning is performed on active states, and when the duration of the inactive state interruption between adjacent active states does not exceed a preset fault-tolerance time window, the corresponding continuous time period is determined as a candidate continuous process segment; the internal state composition of each candidate continuous process segment is checked, and if the candidate continuous process segment contains any two of the active states in the time series, the candidate continuous process segment is determined as a continuous complex drought-heat wave event.
[0015] Further, step S6 includes: calculating the event characteristic parameters of each compound drought-heat wave event based on the joint stress intensity index, calculating the event characteristic parameters of each single stress event based on the degraded single stress intensity index, and constructing an event record set by treating all event characteristic parameters of each event as a structured event record entry; the events include compound drought-heat wave events and single stress events; classifying each continuous compound event into temporal structures based on the relative relationship between the earliest and latest occurrence times of drought stress state and heat stress state in the time series, and writing the corresponding structure type label into the corresponding event record entry; and performing purity screening on the single stress event sample set composed of each single stress event through a temporal neighborhood stripping mechanism to obtain a pure single stress event sample set.
[0016] Furthermore, based on the relative relationship between the earliest and latest occurrence times of drought stress and heat stress states in the time series, each continuous composite event is classified into temporal structures, including: for each identified continuous composite drought-heat wave event, determining the earliest and latest occurrence times of drought stress and heat stress states respectively; classifying the continuous composite drought-heat wave event into different lead types according to the earliest occurrence order of the two stress states within the event process window; classifying the continuous composite drought-heat wave event into different duration types according to the latest occurrence order of the two stress states within the event process window; and classifying the continuous composite drought-heat wave event into different temporal structure types based on the lead type and duration type.
[0017] Furthermore, a purity screening process is performed on the single stress event sample set composed of individual stress events through a time neighborhood stripping mechanism to obtain a pure single stress event sample set. This includes: setting a preset time neighborhood window before the start time and / or after the end time of each single stress event; checking whether there is another type of stress state or a compound stress state that is different from the single stress event type within the preset time neighborhood window; when another type of stress state or a compound stress state is detected within the preset time neighborhood window, the single stress event is determined to be a non-pure single stress event and is removed from the single stress event sample set; otherwise, it is retained as a pure single stress event to obtain a pure single stress event sample set.
[0018] According to one aspect of this invention, the present invention provides a system for quantifying and uniformly identifying the joint stress of a daily-scale combined drought-heat wave event, comprising: a spatiotemporal alignment module for acquiring daily-scale temperature data and land surface moisture data and performing temporal alignment and spatial consistency preprocessing to construct a daily-scale gridded time series dataset with unified spatiotemporal resolution; a single stress intensity index calculation module for performing standardization processing and extreme stress determination on daily temperature data and land surface moisture data based on the daily-scale gridded time series dataset to calculate a daily-scale single stress intensity index; the single stress intensity index includes a heat stress intensity index and a drought stress intensity index; and a joint stress intensity index calculation module for constructing a daily-scale joint stress intensity index based on the daily-scale heat stress intensity index and drought stress intensity index; wherein, the joint stress intensity index is determined only by... When a single stress exists, it degenerates into the corresponding single stress intensity index; a multi-class discrete coding module is used to perform multi-class discrete coding on the daily stress state based on the daily-scale thermal stress intensity index and drought stress intensity index to obtain a multi-class discrete state time series; a drought and heat wave event identification module is used to uniformly identify compound drought-heat wave events on the multi-class discrete state time series based on a preset fault-tolerant time window and time continuity criteria, using a continuous run scanning method; a continuous run scanning method is used to scan continuous time periods containing only a single stress state to achieve the identification of single stress events; a structured event library generation module is used to integrate and process each identified compound drought-heat wave event and single stress event based on the joint stress intensity index and its degenerated single stress intensity index to construct a structured event record set.
[0019] According to one aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory storing program instructions executed by the processor, the processor invoking the program instructions to execute the method for quantifying and uniformly identifying the joint stress of a diurnal combined drought-heat wave event.
[0020] The above technical solution acquires temperature and land surface moisture data, performs temporal alignment and spatial consistency processing, and then uses the spatiotemporally aligned temperature and land surface moisture data to construct a single stress intensity index. Furthermore, it constructs a joint stress intensity index based on diurnal thermal and drought stress intensity indices to characterize the nonlinear synergistic enhancement effect of the two extreme events. Based on a preset fault-tolerant time window and temporal continuity criterion, a continuous run-length scan method is used to uniformly identify compound drought-heat wave events. The continuous run-length scan method is also used to scan continuous time periods containing only a single stress state to achieve the identification of single stress events. The evolutionary structure of continuous compound drought-heat wave events is classified into leading and persistent types to further characterize the temporal evolution structure of continuous events. Simultaneously, a strict temporal neighborhood purity stripping mechanism is constructed to obtain a pure single stress event sample set unaffected by compound background interference.
[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0022] (1) By constructing a joint stress intensity index, the nonlinear synergistic enhancement effect of drought and heat wave can be characterized, thereby solving the problem that the existing technology uses a simple linear superposition method to construct a composite index, which is difficult to reflect the nonlinear synergistic enhancement effect of drought and high temperature on the same spatiotemporal scale;
[0023] (2) The continuous run scanning method is used to identify concurrent complex drought-heat wave events, continuous complex drought-heat wave events, and single stress events, which effectively avoids the event process being fragmented by short-term fluctuations;
[0024] (3) The evolution structure of continuous complex drought-heat wave events is classified into lead type and continuous type to further characterize the temporal evolution structure of continuous events, thereby solving the problem that existing technologies are unable to identify temporal continuous complex events of "drought first, heat wave later" or "heat wave first, drought later", and are unable to characterize the evolution structure of complex disasters;
[0025] (4) Construct a strict time neighborhood purity stripping mechanism to provide reliable control samples for impact assessment and attribution analysis, so as to solve the problem that existing technologies lack reliable benchmarks for subsequent impact assessment and control analysis due to the mixing of compound stress signals in single stress event samples. Attached Figure Description
[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 This is a schematic diagram of the overall process of a method for quantifying and uniformly identifying the joint stress of a daily-scale combined drought-heat wave event provided by an embodiment of the present invention.
[0028] Figure 2 This is a schematic diagram illustrating the construction logic of the single stress intensity index and the combined stress intensity index (CDHS) in an embodiment of the present invention.
[0029] Figure 3 This is a schematic diagram illustrating the principle of multi-class discrete stress state encoding and concurrent and continuous complex drought-heat wave event identification based on the fault-tolerant time window (Gap) mechanism in an embodiment of the present invention.
[0030] Figure 4 This is a schematic diagram of the classification logic of the temporal evolution structure (leading type / persistent type) of continuous complex drought-heat wave events in an embodiment of the present invention.
[0031] Figure 5 This is a schematic diagram of the time neighborhood stripping and filtering process for a pure single event in an embodiment of the present invention. Detailed Implementation
[0032] It should be noted that:
[0033] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be decomposed, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.
[0036] Please refer to the appendix. Figure 1 This invention provides a method for quantifying and uniformly identifying the joint stress of daily-scale combined drought-heat wave events, specifically including the following steps:
[0037] Step S1: Acquire daily-scale temperature data and land surface moisture data, and perform time alignment and spatial consistency preprocessing to construct a daily-scale grid time series dataset with unified spatiotemporal resolution.
[0038] In step S1, daily temperature data for the current study area are acquired. Land surface moisture data This study performs temporal alignment and spatial consistency processing on these two types of data to achieve a unified temporal and spatial resolution, thereby constructing a daily-scale gridded time series dataset with unified spatiotemporal resolution. Here, daily scale refers to an observational / statistical scale with a natural day (single day) as the time unit; that is, daily-scale temperature data and land surface moisture data are continuous time series data with "day" as the time interval. Temperature data can be daily maximum temperature or daily average temperature. Land surface moisture data can be soil moisture content, standardized soil moisture index, or equivalent moisture characterization indicators. In essence, a daily-scale gridded time series dataset refers to a fused dataset of temperature and land surface moisture, with a natural day as the time unit and the grid corresponding to the previously studied area as the spatial unit, which, after temporal alignment and spatial consistency processing, possesses both unified temporal and spatial resolution.
[0039] The above two types of data can be derived from meteorological reanalysis data, ground observation data, remote sensing inversion data, or multi-source fusion data. This invention does not limit the specific source and format of these two types of data; any data capable of characterizing diurnal temperature and moisture conditions is applicable. In this embodiment, the time range for temperature data is from 2000 to 2024. It should be noted that the above time range is merely an example of this embodiment and can be adjusted according to research needs in practical applications.
[0040] Step S2: Based on the daily grid time series dataset, standardize and determine the daily temperature data and land surface moisture data respectively to calculate the daily single stress intensity index.
[0041] In step S2, the single stress intensity index includes the thermal stress intensity index and the drought stress intensity index. The specific steps are as follows: based on the statistical distribution of the corresponding meteorological elements and water elements within a preset historical baseline period, the quantile thresholds of the corresponding meteorological elements and water elements are determined according to a preset probability level (the preset probability level corresponds to the tail characteristics of the statistical distribution). These are used as the high temperature extreme determination threshold and the drought extreme determination threshold. Specifically, for the meteorological elements that characterize the high temperature attribute, the upper quantile is selected to determine the high temperature extreme determination threshold; for the water elements that characterize the drought attribute, the lower quantile or extreme direction quantile is selected to determine the drought extreme determination threshold.
[0042] Furthermore, daily temperature data are compared with high-temperature extreme thresholds to calculate the outlier amount relative to the high-temperature extreme threshold. The portion of the outlier that does not reach the high-temperature extreme threshold is truncated, suppressed, or gated to obtain a thermal stress intensity index. Similarly, daily land surface moisture data are compared with drought extreme thresholds to calculate the outlier amount relative to the drought extreme threshold. The portion of the outlier that does not reach the drought extreme threshold is truncated, suppressed, or gated to obtain a drought stress intensity index. The thermal stress intensity index is a non-negative stress intensity quantification value used to characterize the degree of diurnal thermal extreme stress, and the drought stress intensity index is a non-negative stress intensity quantification value used to characterize the degree of drought extreme stress. The stress intensity quantification values are used to characterize the degree of anomalousness at the current time relative to the historical climate background.
[0043] Please refer to the following: Figure 2 In a preferred embodiment, the thermal stress intensity index With drought stress intensity index Calculate as follows:
[0044]
[0045]
[0046] in, As an indicator of thermal stress intensity, As an indicator of drought stress intensity, , For any grid point in the current study region at the th Daily temperature data and land surface moisture data, and These are the scale parameters used to normalize the temperature data and land surface moisture data. The scale parameters are quantile interval parameters, quantile interval parameters, or equivalent robust scale parameters determined based on historical baseline sample statistics. This is the threshold for determining extreme high temperatures (i.e., the upper quantile threshold). This is the threshold for determining drought extremes (i.e., the lower quantile threshold). When the observed values (i.e., temperature data and land surface moisture data) do not exceed the corresponding thresholds (i.e., the threshold for determining high temperature extremes and the threshold for determining drought extremes), their stress intensity is truncated to 0, thus ensuring that the stress intensity index is a non-negative value.
[0047] For example, in one specific implementation, the historical baseline period is selected as 1961–1990 or 1981–2010. For any grid point in the current study area, based on the temperature sample distribution within this historical baseline period, the 90th percentile is taken as the threshold for determining high-temperature extremes. Based on the distribution of moisture samples, the 10th percentile is taken as the threshold for determining drought extremes. Meanwhile, scale parameters and The interquartile range (IQR) parameter, which is the difference between the 75th percentile and the 25th percentile, was chosen as the corresponding variable within the historical baseline period. IQR was used instead of standard deviation because meteorological data often do not follow a strict normal distribution. IQR is more robust to outliers, so as to reduce the impact of extreme outliers on the estimation of scale parameters.
[0048] It should be understood that the above-mentioned threshold selection method and scale parameter form are only a preferred embodiment of the present invention. Without departing from the technical concept of the present invention, other quantile thresholds, standard deviation scales or equivalent scale parameter forms can also be used to normalize outliers exceeding the threshold.
[0049] Step S3: Construct a combined stress intensity index at the daily scale based on the daily thermal stress intensity index and the drought stress intensity index.
[0050] In step S3, the joint stress intensity index is used to characterize the synergistic enhancement nonlinear superposition effect of drought stress and heat stress on the same time scale. When only a single stress exists, the joint stress intensity index degenerates into the corresponding single stress intensity index. When both types of stress exist simultaneously, the joint stress intensity index contains a multiplicative enhancement term, which is used to characterize the synergistic amplification effect between the two types of stress.
[0051] In a preferred implementation, based on the thermal stress intensity index With drought stress intensity index Through nonlinear coupling function Construct a joint stress intensity index:
[0052] (1) ;in, This indicates the combined stress intensity index when only thermal stress exists (without drought stress). Indicator of thermal stress intensity This indicates the combined stress intensity index when only drought stress exists (without heat stress). This indicates the intensity of drought stress.
[0053] (2) When It has a synergistic enhancement relative to linear superposition terms, making .in, It represents the joint stress intensity index constructed through nonlinear coupling when thermal stress and drought stress coexist.
[0054] (3) The formula corresponding to the Compound Drought–Heat Stress (CDHS) index is as follows:
[0055]
[0056] in, t represents the combined stress intensity index, and t represents time.
[0057] For example, in one embodiment, for a grid point in the current study area, if the highest observed temperature on a certain day is 38°C, the corresponding high temperature extreme determination threshold is... =35℃, and scale parameters =2℃, then the thermal stress intensity index for that day can be calculated. If the drought stress intensity index on the same day The combined stress intensity index for that day is:
[0058] ,
[0059] This shows that when two types of stress coexist, the joint stress intensity index contains a significant synergistic amplification effect, which can effectively characterize the enhanced impact of the combined extreme event relative to the single extreme event.
[0060] It should be understood that the above-mentioned nonlinear coupling function form and parameter values are only a preferred embodiment of the present invention. Without departing from the technical concept of the present invention, other nonlinear coupling function forms with synergistic enhancement characteristics can also be used to construct the joint stress intensity index.
[0061] Step S4: Based on the daily thermal stress intensity index and drought stress intensity index, perform multi-class discrete coding on the daily stress state to obtain the multi-class discrete state time series.
[0062] In step S4, the daily stress state is determined based on the daily heat stress intensity index and drought stress intensity index. Based on the determination of the stress state, the daily stress state is divided into at least four mutually exclusive discrete state categories. These discrete state categories include at least: no stress state, drought-only stress state, heat-only stress state, and simultaneous drought and heat stress. State; assigning corresponding discrete coding identifiers to discrete state categories to form a multi-class discrete state time series for subsequent event identification. Discrete stress states include at least the following four mutually exclusive states:
[0063]
[0064] Through the above classification and encoding process, the continuous diurnal stress intensity time series constructed from thermal stress intensity indices, drought stress intensity indices, and combined stress intensity indices are transformed into multi-class discrete stress state time series for subsequent event identification and structural analysis. It should be noted that although the combined stress intensity index is not involved in extreme stress determination, it is still a part of the multi-class discrete stress state time series. As a characterization of continuous stress intensity, the combined stress intensity index is used for subsequent intensity accumulation quantification and event feature calculation of composite events.
[0065] Step S5: On the time series of multi-class discrete stress states, based on the preset fault-tolerant time window and time continuity criterion, the continuous run scanning method is used to uniformly identify the compound drought-heat wave event; the continuous run scanning method is used to scan the continuous time period containing only a single stress state to achieve the identification of a single stress event.
[0066] Please refer to the following: Figure 3Complex drought-heat wave events include at least concurrent complex drought-heat wave events and continuous complex drought-heat wave events. Single stress events include drought events and heat wave events. The unified identification of complex drought-heat wave events and single stress events includes the following sub-steps:
[0067] Step S51, identify concurrent drought-heat wave events, including: in a multi-class discrete state time series, classify discrete stress states (i.e., the composite stress state) is used as the target state, and a continuous run scan is performed on the target state; when the target state appears continuously in time and its continuous duration is not less than a preset minimum duration threshold. At that time, the corresponding continuous time period is determined to be a concurrent complex drought-heat wave event.
[0068] Step S52, identifying continuous complex drought-heat wave events, including: in a multi-class discrete state time series, classifying discrete stress states ( (1) represents drought stress only, 2 represents heat stress only, and 3 represents combined stress) are collectively referred to as active states, and discrete stress states are also considered. (i.e., no-stress state) is defined as an inactive state. A continuous run scan is performed on the active states, and the duration of the inactive state interruption between adjacent active states does not exceed a preset fault-tolerant time window. When the corresponding continuous time period is determined as a candidate continuous process segment, the internal state composition of each candidate continuous process segment is examined. If the candidate continuous process segment contains any two of the activity states in the time series (e.g., 1-2-3, 1-2-2, 1-3-3, 2-3-3, 1-3-0-3), then the candidate continuous process segment is determined as a continuous complex drought-heat wave event.
[0069] It should be noted that the preset minimum duration threshold and preset fault tolerance time window The value of can be set according to the climate characteristics of the study area or the application requirements, and is not limited here. In a preferred embodiment, Ideally, 3 days. Preferably 1 to 3 days. For example, when set "Timing" indicates that a compound event is identified only when a state of stress lasts for three consecutive days or more; when set... The concept of "timing" allows for single-day interruptions in the non-stress state during a continuous disaster process, while still identifying them as the same continuous composite event process. This effectively avoids the event process being incorrectly segmented due to short-term weather fluctuations.
[0070] Step S53, identifying a single stress event, includes: in a multi-class discrete state time series, identifying a single stress event by performing a run-length scan on a continuous time period containing only a single stress state.
[0071] It should be noted that, based on the multi-class discrete stress state time series in step S4, in addition to the concurrent and continuous compound drought-heat wave events identified in step S5, a drought event set consisting of several drought events and a heat wave event set consisting of several heat wave events can be identified by performing run scans on continuous time periods containing only a single stress state. The drought event set and the heat wave event set together form a candidate set of single stress events. Each single stress event in the candidate set of single stress events and each compound drought-heat wave event are used as the object for subsequent event feature calculation, statistical analysis and purity screening.
[0072] Step S6: Based on the joint stress intensity index and the degenerated single stress intensity index, each identified composite drought-heat wave event and single stress event is integrated and processed to construct a structured event record set.
[0073] In step S6, the integration process includes event feature parameter calculation, structure classification, and purity screening to construct a structured set of event records containing time, intensity, and type labels. Specifically, it includes the following sub-steps:
[0074] Step S61: Calculate the event characteristic parameters of each composite drought-heat wave event based on the joint stress intensity index, calculate the event characteristic parameters of each single stress event based on the single stress intensity index degenerated from the joint stress intensity index, and construct an event record set by taking all the event characteristic parameters of each event as a structured event record entry.
[0075] In step S61, each event includes concurrent compound drought-heat wave events, continuous compound drought-heat wave events, single drought events, and single heat wave events. First, the start and end times of each event are determined. Then, event characteristic parameters are calculated based on the start and end times. The event characteristic parameters include at least: duration, cumulative intensity, and peak intensity. The specific calculation formula is as follows:
[0076] Event duration:
[0077]
[0078] in, Indicates the duration of the event. Indicates the end time of the event. Indicates the start time of the event.
[0079] Cumulative Event Intensity: Constructing a Unified RobustCompound Drought–Heatwave Intensity Index (UR-CDHWI) to measure cumulative event intensity.
[0080]
[0081] in, Indicates the cumulative intensity of the event. This represents the intensity at time t.
[0082] Average event intensity:
[0083]
[0084] in, Indicates the average intensity of the event. Indicates the total duration of the event.
[0085] Peak intensity of the event:
[0086]
[0087] in, Indicates the peak intensity of the event.
[0088] Each event's start time, end time, duration, cumulative intensity, average intensity, peak intensity, and event type label are stored as a structured event record entry in an event record set. On a statistical periodic scale, the structured event record entries in this set are summarized and statistically analyzed to obtain statistical indicators such as the frequency of compound drought-heat wave events or single stress events in the current study area.
[0089] Step S62: Based on the relative relationship between the earliest and latest occurrence times of drought stress and heat stress in the time series, classify the temporal structure of each continuous composite event and write the corresponding structure type label into the corresponding event record entry.
[0090] Please refer to the following: Figure 4 For each continuous complex drought-heat wave event identified in step S5, the earliest occurrence time of drought stress state is determined. With the latest time of occurrence The earliest occurrence of thermal stress With the latest time of occurrence Based on the earliest occurrence order of the two stress states within the event process window, the leader type of the continuous complex drought-heat wave event is determined; based on the latest occurrence order of the two stress states within the event process window, the duration type of the continuous complex drought-heat wave event is determined; based on the leader type and duration type, each continuous complex event is classified into temporal structure categories to divide the continuous complex drought-heat wave event into at least four different temporal structure types, and the corresponding structure type labels are written into the corresponding event record entries.
[0091] Step S63: The purity of the single stress event sample set composed of each single stress event is screened through the time neighborhood stripping mechanism to obtain a pure single stress event sample set.
[0092] Please refer to the following: Figure 5 After identifying the drought and heat wave event sets, a purity screening process is performed on individual stress events: a preset time neighborhood window is set before and / or after the start time of each individual stress event; it is checked whether there is another type of stress state or a compound stress state that is different from the type of individual stress event within the preset time neighborhood window; when another type of stress state or a compound stress state is detected within the preset time neighborhood window, the individual stress event is determined to be a non-pure individual stress event and is removed from the pure individual stress event sample set; otherwise, it is retained as a pure individual stress event to obtain a pure individual stress event sample set. Understandably, through the above purity stripping process, statistically independent pure drought and pure heat wave event sets can be effectively constructed, thus providing a reliable benchmark for subsequent assessment of the nonlinear impact increment of compound events relative to individual events.
[0093] In some feasible embodiments, to achieve an intuitive expression and regional comparison of the spatiotemporal characteristics of composite events, step S6 of the present invention further includes step S64: spatializing the grid-scale statistical indicators based on the event record set and generating a rasterized result product. Specifically, for each grid point, concurrent composite drought-heat wave events and continuous composite drought-heat wave events are summarized and statistically analyzed within a preset statistical period to obtain indicators such as annual occurrence frequency, average duration, average cumulative intensity, and average intensity. The annual occurrence frequency can be defined as the total number of events within the statistical period divided by the number of statistical years; the average duration is the average of the durations of all events at that grid point within the statistical period; the average cumulative intensity is the average of the cumulative intensities of all events within the statistical period; and the average intensity is the average of the average intensities of all events within the statistical period. Further, the above statistical results can be output as a raster file (e.g., GeoTIFF or other equivalent formats) according to a unified spatial grid to form a spatial distribution map for visualization, regional comparison, and subsequent application evaluation.
[0094] It should be understood that the above spatial statistical indicators, statistical periods and output formats are merely examples. Those skilled in the art can use equivalent statistical methods or equivalent raster formats to output the corresponding spatialization results without departing from the technical concept of this invention.
[0095] Based on the same technical concept as the foregoing embodiments, this invention also provides a system for quantifying and uniformly identifying the joint stress of daily-scale combined drought-heat wave events, comprising: a spatiotemporal alignment module for acquiring daily-scale temperature data and land surface moisture data and performing temporal alignment and spatial consistency preprocessing to construct a daily-scale grid time series dataset with unified spatiotemporal resolution; a single stress intensity index calculation module for standardizing and determining extreme stresses on daily temperature data and land surface moisture data based on the daily-scale grid time series dataset to calculate a daily-scale single stress intensity index; the single stress intensity index includes a heat stress intensity index and a drought stress intensity index; and a joint stress intensity index calculation module for constructing a daily-scale joint stress intensity index based on the daily-scale heat stress intensity index and drought stress intensity index; wherein, the joint stress intensity index... When only a single stress exists, it degenerates into the corresponding single stress intensity index; a multi-class discrete coding module is used to perform multi-class discrete coding on the daily stress state based on the daily-scale thermal stress intensity index and drought stress intensity index to obtain a multi-class discrete state time series; a drought and heat wave event identification module is used to uniformly identify compound drought-heat wave events on the multi-class discrete state time series based on a preset fault-tolerant time window and time continuity criteria, using a continuous run scanning method; a continuous run scanning method is used to scan continuous time periods containing only a single stress state to achieve the identification of single stress events; a structured event library generation module is used to integrate and process each identified compound drought-heat wave event and single stress event based on the joint stress intensity index and its degenerated single stress intensity index to construct a structured event record set.
[0096] Based on the same technical concept as the foregoing embodiments, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores program instructions that are executed by the processor, and the processor invokes the program instructions to execute the method for quantifying and uniformly identifying the joint stress of a diurnal complex drought-heat wave event.
[0097] In summary, the above embodiments acquire temperature data and land surface moisture data, perform temporal alignment and spatial consistency processing, and then use the spatiotemporally aligned temperature data and land surface moisture data to construct a single stress intensity index. Furthermore, a joint stress intensity index is constructed based on diurnal thermal stress intensity and drought stress intensity indices to characterize the nonlinear synergistic enhancement effect of the two types of extreme events. This addresses the problem in existing technologies that use simple linear superposition to construct composite indices, which are insufficient to reflect the nonlinear synergistic enhancement effect of drought and high temperature at the same spatiotemporal scale. A continuous run-length scan method is used to identify concurrent complex drought-heat wave events, continuous complex drought-heat wave events, and single stress events, effectively avoiding the fragmentation of event processes by short-term fluctuations and significantly improving the identification of complex extreme events. The assessment aims to improve accuracy and robustness. It classifies the evolutionary structure of continuous drought-heat wave events into lead-type and persistent types to further characterize the temporal evolution of continuous events. This addresses the current limitations of existing technologies in identifying temporally continuous composite events where "drought precedes heat wave" or "heat wave precedes drought," thus failing to characterize the evolutionary structure of composite disasters. Simultaneously, a rigorous temporal neighborhood purity stripping mechanism is constructed to obtain a pure single-stress event sample set unaffected by composite background interference. This provides stable and reproducible technical support for the monitoring, assessment, and impact attribution analysis of composite extreme events, thereby resolving the problem that existing technologies lack reliable benchmarks for subsequent impact assessments and control analyses due to the presence of composite stress signals mixed in single-stress event samples.
[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.
Claims
1. A method for quantifying and uniformly identifying the joint stress of diurnal-scale combined drought-heat wave events, characterized in that, include: S1: Acquire daily-scale temperature data and land surface moisture data and perform time-aligned and spatially consistent preprocessing to construct a daily-scale grid time series dataset with unified spatiotemporal resolution; S2: Based on the daily grid time series dataset, the daily temperature data and land surface moisture data are standardized and extreme stress are determined to calculate the daily single stress intensity index. The single stress intensity index includes the thermal stress intensity index and the drought stress intensity index; S3: Construct a daily-scale joint stress intensity index based on the daily-scale thermal stress intensity index and drought stress intensity index; wherein, the joint stress intensity index degenerates into the corresponding single stress intensity index when only a single stress exists; S4: Based on the daily thermal stress intensity index and drought stress intensity index, the daily stress state is multi-class discretely encoded to obtain the multi-class discrete state time series. S5: On the multi-class discrete state time series, based on the preset fault-tolerant time window and time continuity criterion, the continuous run scanning method is used to uniformly identify the compound drought-heat wave event; the continuous run scanning method is used to scan the continuous time period containing only a single stress state in order to realize the identification of a single stress event. S6: Based on the joint stress intensity index and its degenerate single stress intensity index, each identified composite drought-heat wave event and single stress event is integrated and processed to construct a structured event record set.
2. The method for quantifying and uniformly identifying the joint stress of diurnal drought-heat wave events as described in claim 1, characterized in that, Step S2 includes: Based on the statistical distribution of corresponding meteorological and moisture elements within a preset historical baseline period, the thresholds for determining extreme high temperatures and extreme droughts are determined. Based on the aforementioned high temperature extreme determination threshold, the daily temperature data is standardized and extreme stress is determined to obtain the daily heat stress intensity index. Based on the drought extreme determination threshold, the daily land surface moisture data are standardized and extreme stress is determined to obtain the daily drought stress intensity index.
3. The method for quantifying and uniformly identifying the joint stress of diurnal drought-heat wave events as described in claim 2, characterized in that, The daily temperature and land surface moisture data are standardized and extreme stress is determined using the following formula: ， ， in, As an indicator of thermal stress intensity, As an indicator of drought stress intensity, and For any grid point in the current study region at the th Daily temperature data and land surface moisture data, The threshold for determining extreme high temperatures. The threshold for determining drought extremes. , These are the scale parameters for scaling temperature data and land surface moisture data, respectively.
4. The method for quantifying and uniformly identifying the joint stress of diurnal-scale combined drought-heat wave events as described in claim 1, characterized in that, Step S4 includes: The daily stress status is determined based on the daily heat stress intensity index and drought stress intensity index; Based on the determination of stress status, the daily stress status is divided into at least four mutually exclusive discrete status categories; the discrete status categories include at least no stress status, drought stress only status, heat stress only status, and combined stress status. Assign corresponding discrete code identifiers to the discrete state categories to form multi-class discrete state time series.
5. The method for quantifying and uniformly identifying the joint stress of diurnal combined drought-heat wave events as described in claim 1, characterized in that, On the multi-class discrete state time series, based on a preset fault-tolerant time window and time continuity criterion, a continuous run-length scan method is used to uniformly identify complex drought-heat wave events, including: In the multi-class discrete state time series, the composite stress state is used as the target state for continuous run-length scanning; When the target state occurs continuously in time and the duration of the continuous occurrence is not less than a preset minimum duration threshold, the corresponding continuous time period is determined as a concurrent complex drought-heat wave event. In the multi-class discrete state time series, drought-only stress state, heat-only stress state and combined stress state are defined as active states, and no-stress state is defined as inactive state. Perform continuous run scans on the active states. If the duration of the interruption between adjacent active states is no more than a preset fault tolerance time window, the corresponding continuous time period is determined as a candidate continuous process segment. Examine the internal state composition of each candidate continuous process segment. If the candidate continuous process segment contains any two of the activity states in the time series, then the candidate continuous process segment is determined to be a continuous complex drought-heat wave event.
6. The method for quantifying and uniformly identifying the joint stress of diurnal combined drought-heat wave events as described in claim 5, characterized in that, Step S6 includes: The event characteristic parameters of each combined drought-heat wave event are calculated based on the joint stress intensity index, and the event characteristic parameters of each single stress event are calculated based on the degraded single stress intensity index. All event characteristic parameters of each event are treated as a structured event record entry to construct an event record set. The events include combined drought-heat wave events and single stress events. Based on the relative relationship between the earliest and latest occurrence times of drought stress and heat stress in the time series, each continuous composite event is classified according to its temporal structure, and the corresponding structure type label is written into the corresponding event record entry. The purity of the single stress event sample set, which consists of individual stress events, is screened by a time neighborhood stripping mechanism to obtain a pure single stress event sample set.
7. The method for quantifying and uniformly identifying the joint stress of diurnal-scale combined drought-heat wave events as described in claim 6, characterized in that, Based on the relative earliest and latest occurrence times of drought and heat stress states in the time series, the temporal structure of each continuous composite event is classified, including: For each identified continuous complex drought-heat wave event, the earliest and latest occurrence times of drought stress and heat stress are determined respectively. Based on the earliest occurrence order of the two types of stress states within the event process window, the continuous complex drought-heat wave event is divided into different lead types; Based on the latest order of occurrence of the two types of stress states within the event process window, the continuous complex drought-heat wave event is divided into different duration types; Based on the leading type and the persistence type, the continuous complex drought-heat wave event is divided into different time-series structure types.
8. The method for quantifying and uniformly identifying the joint stress of diurnal-scale combined drought-heat wave events as described in claim 6, characterized in that, A purity screening process is performed on the single stress event sample set composed of individual stress events using a temporal neighborhood stripping mechanism to obtain a pure single stress event sample set, including: Set a preset time neighborhood window before the start time and / or after the end time of each individual coercion event; Check whether there is another type of stress state or a composite stress state that is different from the single stress event type within the preset time neighborhood window; When another type of stress state or a compound stress state is detected within the preset time neighborhood window, the single stress event is determined to be a non-pure single stress event and is removed from the single stress event sample set; otherwise, it is retained as a pure single stress event to obtain a pure single stress event sample set.
9. A system for quantifying and uniformly identifying the joint stress of diurnal-scale combined drought-heat wave events, characterized in that, include: The spatiotemporal alignment module is used to acquire daily-scale temperature data and land surface moisture data and perform temporal alignment and spatial consistency preprocessing to construct a daily-scale grid time series dataset with unified spatiotemporal resolution. The single stress intensity index calculation module is used to perform standardization processing and extreme stress determination on daily temperature data and land surface moisture data based on the daily grid time series dataset, so as to calculate the daily single stress intensity index. The single stress intensity index includes the thermal stress intensity index and the drought stress intensity index; The joint stress intensity index calculation module is used to construct a daily-scale joint stress intensity index based on the daily-scale thermal stress intensity index and drought stress intensity index; wherein, the joint stress intensity index degenerates into the corresponding single stress intensity index when only a single stress exists. The multi-class discrete coding module is used to perform multi-class discrete coding on the daily stress state based on the daily thermal stress intensity index and drought stress intensity index to obtain the multi-class discrete state time series. The drought and heat wave event identification module is used to uniformly identify compound drought-heat wave events based on a preset fault-tolerant time window and time continuity criteria on the multi-class discrete state time series, and to scan continuous time periods containing only a single stress state using the continuous run scanning method, so as to achieve the identification of single stress events. The structured event database generation module is used to integrate and process each identified composite drought-heat wave event and single stress event based on the joint stress intensity index and its degenerate single stress intensity index, in order to construct a structured event record set.
10. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores program instructions that are executed by the processor, and the processor invokes the program instructions to execute a method for quantifying and uniformly identifying the joint stress of a diurnal complex drought-heat wave event as described in any one of claims 1 to 8.